The Philosophy of Animal Minds

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THE PHILOSOPHY OF ANIMAL MINDS

This volume is a collection of fourteen new essays by leading philosophers on issues concerning the nature, the existence, and our knowledge of animal minds. The nature of animal minds has been a topic of interest to philosophers since the origins of philosophy, and recent years have seen significant philosophical engagement with the subject. However, there is no volume that represents the current state of play in this important and growing field. The purpose of this volume is to highlight the state of the debate. The issues which are covered include whether and to what degree animals think in a language or in iconic structures, possess concepts, are conscious and self-aware, metacognize, attribute states of mind to others, and have emotions, as well as issues pertaining to our knowledge of mental states in animals and the scientific standards for attributing them. r o b e r t w . l u r z is Associate Professor in the Department of Philosophy, Brooklyn College of The City University of New York.

THE PHILOSOPHY OF ANIMAL MINDS edited by ROBERT W. LURZ

CAMBRIDGE UNIVERSITY PRESS

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Dubai, Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521885027 © Cambridge University Press 2009 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published in print format 2009 ISBN-13

978-0-511-65181-6

eBook (NetLibrary)

ISBN-13

978-0-521-88502-7

Hardback

ISBN-13

978-0-521-71181-4

Paperback

Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.

For Mary Jane, William and James for their love and patience

Contents

List of contributors Acknowledgments

page ix xi

The philosophy of animal minds: an introduction



Robert W. Lurz





What do animals think? Dale Jamieson



Attributing mental representations to animals



Eric Saidel



Chrysippus’ dog as a case study in non-linguistic cognition



Michael Rescorla



Systematicity and intentional realism in honeybee navigation



Michael Tetzlaff and Georges Rey



Invertebrate concepts confront the generality constraint (and win)



Peter Carruthers



A language of baboon thought?



Elisabeth Camp



Animal communication and neo-expressivism



Andrew McAninch, Grant Goodrich, and Colin Allen



Mindreading in the animal kingdom



Jos´e Luis Berm´udez



The representational basis of brute metacognition: a proposal Jo¨elle Proust

vii



viii

Contents

 Animals, consciousness, and I-thoughts



Rocco J. Gennaro



Self-awareness in animals



David DeGrazia



The sophistication of non-human emotion



Robert C. Roberts



Parsimony and models of animal minds



Elliott Sober

 The primate mindreading controversy: a case study in simplicity and methodology in animal psychology



Simon Fitzpatrick

Glossary of key terms References Index

  

Contributors

colin allen Professor of History and Philosophy of Science and Professor of Cognitive Science, Indiana University jos´e luis berm u´ dez Professor of Philosophy, Washington University elisabeth camp Assistant Professor of Philosophy, University of Pennsylvania peter carruthers Professor of Philosophy, University of Maryland david d e grazia Professor of Philosophy, George Washington University simon fitzpatrick Research Associate, AHRC Culture and the Mind Project, University of Sheffield rocco j. gennaro Professor of Philosophy, The University of Southern Indiana grant goodrich Doctoral Student in the History and Philosophy of Science, Indiana University dale jamieson Professor of Environmental Studies and Philosophy Affiliated Professor of Law, New York University robert w. lurz Associate Professor of Philosophy, Brooklyn College, City University of New York andrew m c aninch Doctoral Student in Philosophy, Indiana University jo¨e lle proust Director of Research at CNRS, Institut Jean-Nicod michael rescorla Assistant Professor of Philosophy, University of California, Santa Barbara georges rey Professor of Philosophy, University of Maryland ix

x

Contributors

robert c. roberts Distinguished Professor of Ethics, Baylor University eric saidel Assistant Professor of Philosophy, George Washington University elliott sober Hans Reichenbach Professor and William Vilas Research Professor, University of Wisconsin michael tetzlaff Visiting Instructor, University of Maryland

Acknowledgments

As with most projects, this one started out as a fairly rough idea that was eventually molded and shaped into a recognizable form by various people along the way. During its early stages, I was given invaluable advice about its direction and content by two anonymous referees, Colin Allen, Peter Carruthers, David Chalmers, Rocco Gennaro, and Uriah Kriegel. While in the thick of the editing process, I was helped immensely by the insightful comments and suggestions of Jonathan Adler, Daniel Campos, Emily Michael, Mathew Moore, and Saam Trivedi. The greatest help by far, however, was from the contributors, who met deadlines, actively communicated with each other and myself, effectively responded to (and graciously tolerated) my numerous questions and comments, and (despite all that) produced original work of the highest quality. I thank each and every one of you. I wish also to thank Brooklyn College and my department for a much needed reduction in my teaching load during the spring semester. Finally, I wish to thank Gillian Dadd and Malcolm Todd for their helpful editorial guidance and, above all, Hilary Gaskin for her faith in and support for the volume from beginning to end.

xi

The philosophy of animal minds: an introduction Robert W. Lurz

1. introduction The minds of animals has been an abiding topic in philosophy since its earliest beginnings. Some may find this surprising. After all, a fairly common picture of the philosopher is someone (in a darkened study) ruminating on the nature of the human mind, or on the mind of God, or on some other abstruse idea, but certainly not on the minds of cats, dogs, and honeybees. As common as this picture may be, however, it does not paint an entirely accurate portrait. Philosophers have thought long and hard about the minds of animals and have held and defended significant and influential views on the topic. Moreover, in the past ten years or so there has been an unprecedented amount of interest among philosophers in animal minds, with numerous publications and conferences dedicated to the subject. The level of interest and publication has reached a critical mass and has sustained itself long enough that it is now appropriate to say that the philosophy of animal minds is a field in its own right. The purpose of this volume is to highlight the state of the art in the field by bringing together a collection of new and cutting-edge essays by the best and brightest philosophers. Since the essays in this volume have been shaped by various lines in the rich history of philosophical thought on animal minds, I provide a brief (albeit, vastly incomplete) sketch of some of the most important and influential ideas and arguments in this history, as well as a road map to the volume itself. 2. historical debates Historically the central issue in philosophy concerning animal minds was whether animals possessed thought and reason. Aristotle famously defined Portions of this introduction are from Lurz (). I wish to thank the editors of the Internet Encyclopedia of Philosophy for their kind permission to reproduce the material here. I also wish to thank Peter Carruthers for his helpful comments on an earlier draft of this introduction.





The philosophy of animal minds

human beings as rational animals thereby denying thought and reason to non-human animals (see Nicomachean Ethics, book , chap.  and De Anima, book , chap. ). His chief reason for doing so was his belief that animals, due largely to their failure to speak, were incapable of grasping genuinely abstract concepts and general propositions. For centuries afterward, Aristotle’s denial of thought and reason to animals provoked numerous debates among philosophers. Some, such as Chrysippus, Augustine, Aquinas, Leibniz, and Kant largely argued in favor of the Aristotelian position, while others, such as Theophrastus, Porphyry, Galen, Gassendi, and Locke, largely argued against it. The debate reached its fullest expression in the seventeenth and eighteenth centuries in the figures of Ren´e Descartes and David Hume. Descartes went to the extreme of the Aristotelian position and notoriously denied all types of mentality – reason, thought, and consciousness – to animals. Animals, for Descartes, were nothing more than sophisticated biological machines. Descartes gave two general arguments in support of his “beast-machine” doctrine (/, chap. ). The first rested upon his observation that animals appear incapable of transferring their skills in one domain (e.g., migration) to any other domain (e.g., locating new habitats). This failure to transfer or generalize, Descartes argued, proved that the intelligent-like behaviors of animals were due not to reason – that is, “a universal instrument which can be used in all kinds of situations” – but to naturally endowed instincts and reflexes. Descartes’s second reason rested upon the claim that animals lack speech. Like Aristotle, Descartes denied speech to animals, pointing to the fact that they never “use words or put together other signs, as we do in order to declare our thoughts to others.” The absence of speech, Descartes reasoned, could only be explained in terms of animals lacking what speech 

 



Aristotle acknowledged that animals possessed sensory perception, desires, memory, imagination, and even emotions. According to Sorabji (), Aristotle was able to ascribe emotions to animals, despite their lacking thought, by taking emotion in animals to be a kind of perception. See Roberts (chapter ) for a recent perception-based account of emotions in animals. See Sorabji () for an excellent account of the early history of the philosophical debate over animal thought and reason. It is consistent with the Cartesian position to allow various types of learning in animals (e.g., imprinting, song learning, associative learning, etc.) so long as they do not involve thought or consciousness. The fact that some kinds of associative learning have been demonstrated in the spinal chords of rats without brains strongly suggests that such forms of learning exist in animals (see Grau []). Descartes was aware that animals produce various vocalizations but argued (much in the Aristotelian tradition) that their function was merely to express “passions,” not thought. See McAninch, Goodrich, and Allen (chapter ) for the latest on the modern debate over expressive versus referential theories of animal calls.

The philosophy of animal minds: an introduction



expressed – thought. From this, he went on to conclude that animals also lacked all forms of consciousness, since, for Descartes, thought was taken (in the unusually wide sense) as the very object of conscious awareness – it was, as he writes in the Principles, “everything which we are aware of as happening within us, in so far as we have awareness of it” (/, p. ). The chief problem for Descartes’s position has always been to explain animal behavior without attributing mentality. As noted above, Descartes attempted to do this by appealing to mechanical reflexes and instincts in animals. But it seemed to a number of his contemporaries (as well as to a number of philosophers and scientists today) that this cannot be done for certain types of animal behaviors. As Arnauld famously replied to Descartes, It seems incredible that it can come about, without the assistance of any soul [i.e., without any conscious awareness], that the light reflected from the body of a wolf onto the eyes of a sheep should move the minute fibers of the optic nerves, and that on reaching the brain this motion should spread the animal spirits throughout the nerves in the manner necessary to precipitate the sheep’s flight. (Quoted in Descartes [/], p. .)

However, as we shall see later in this introduction and in a number of the essays in this volume, it is anything but obvious what (if any) types of behaviors in animals require an explanation in terms of thought, reason, or consciousness. Equally as famous as Descartes’s denial of mentality to animals was David Hume’s (/) proclamation that “no truth appears to me more evident, than that beasts are endow’d with thought and reason as well as men” (p. ). The type of thought that Hume had in mind here was belief, which he defined as a “lively idea” or “image” caused by (or associated with) a prior sensory experience (p. ). Hume defined “reason” as a mere disposition or instinct to form associations among such ideas on the basis of past experience. He argued by analogy that since animals behave in ways that closely resemble the behaviors of human beings, which we know introspectively to be accompanied by the association of ideas, it is likely that animals too behave as a result of forming similar associations among ideas in their minds. This argument, Hume claimed, not only described the type of thought process that ordinary persons go through in ascribing thought and reason to animals, it provided an “incontestable” proof that animals possess thought and reason in the same manner as human beings. The only difference between the



The philosophy of animal minds

thought and reasoning of animals and humans, Hume held, was a matter of degree. There are two well-known problems with Hume’s theory. First, although it may be true, as Hume held, that belief and reasoning in animals are constituted by lively images and their associations, many types of beliefs and reasoning in humans are not. We can, for example, form beliefs about all sorts of abstract matters (e.g., about numbers, complex relations, nonEuclidean spaces) of which it is impossible to form an image, as well as engage in many forms of non-associative reasoning. It is difficult, therefore, to understand how human belief and reasoning could be, as Hume held, just a difference in degree from animal belief and reasoning. The other problem with Hume’s theory is that his analogical argument appears to lead to an objectionable form of anthropomorphism. After all, toy robotic dogs, computers, radios, heat-seeking missiles, and even animated circles and squares on a computer screen behave in ways that (at times) strike us as resembling the ways that we behave when we have associated ideas presented to our consciousness. But few would take such resemblances alone as incontestable proof that these objects too act as a result of associated ideas presented to their consciousness.

3. questions and approaches in the philosophy of animal minds From this very brief historical sketch, two distinct types of questions and approaches emerge which, as we shall see, carry through to the contemporary debates on animal minds and are used (in part) to organize the essays in this volume. Philosophical questions concerning animal minds generally come in two (non-exclusive) types: metaphysical and epistemological. Metaphysical questions are questions about what kinds of minds animals possess or could possess consistent with the facts about their behaviors, brains, environments, and histories. As we have seen, the focal metaphysical question in the history of the field has been whether animals could think and reason given that they cannot speak. Epistemological questions, on the other hand, are questions about our knowledge and understanding of animal minds. Such questions can be either normative or descriptive 

Hume went on to argue on similar analogical grounds that animals possess various types of emotions, such as pride, humility, love, and hatred (/, book II, part , sect.  and part , sect. ). See Roberts (chapter ) for an importantly different account of the existence and limits of emotions in animals.

The philosophy of animal minds: an introduction



in import. They can be about what justifies our attributions of mentality to animals, for example, or about how we in fact go about attributing mentality to animals – although the answers may be the same for both, as was seen in the case of Hume’s analogical argument. When considering these types of questions, philosophers tend to take a top-down approach (as exemplified by Aristotle and Descartes) or a bottomup approach (as exemplified by Hume), or some combination of both. The top-down approach begins with reflection upon mentality in humans, with special emphasis on how such mentality is expressed and described in language. The approach takes the human mind as the paradigm and judges animals more likely to have minds the closer they approximate this paradigm. The bottom-up approach, on the other hand, begins with taking our intuitively plausible (or, in some versions, our scientifically informed) ascriptions of mentality to animals at face value and proceeds with designing a theory of animal minds, such as Hume’s theory of ideas, that aims to explain the truth of such ascriptions. Each of these approaches has its potential problems and distinctive virtues. What is yet to be seen is whether one of them, or some combination of both, can yield an account that can satisfactorily answer the various metaphysical and epistemological questions that define the field. With this in mind, let us turn to the contemporary philosophical debates over animal minds. 4. the contemporary debate over animal thought and reason There are three main types of arguments in contemporary philosophy for animal thought and reason: the argument from intentional systems theory (Dennett []), the argument from common-sense functionalism (Carruthers []; Fodor []; Stich []), and the argument from science (Allen and Bekoff []; Berm´udez [a]). The intentional systems theory consists of two general ideas. The first is that our ordinary mental-state concepts, such as our concepts belief, desire, and perceiving, are theoretical concepts whose identity and existence are determined by a common-sense theory or folk psychology. In many cases, we apply our folk psychology to animals to predict and make sense of their behaviors, and when we do, we view animals as intentional systems and take up what Dennett calls the intentional stance toward them. The second important idea of the intentional systems theory is its instrumentalist interpretation of folk psychology. On the instrumentalist interpretation, what it



The philosophy of animal minds

is for a creature to have mental states is simply for its behaviors to be richly and voluminously predicted by the principles of our folk psychology. Therefore, according to the intentional systems theory, the fact that much of animal behavior is successfully predicted from the intentional stance makes animals genuine thinkers and reasoners. The chief objection to this argument, similar to the one raised against Hume’s analogical argument, is that is seems to lead to an objectionable form of anthropomorphism. One can, after all, effectively predict the behaviors of heat-seeking missiles and even the class of thermostats by taking up the intentional stance toward them. Common-sense functionalism, on the other hand, avoids this problem by taking a realist and functionalist interpretation of folk psychology. On this interpretation, for a subject to have mental states is for the subject to have in his brain a variety of discrete internal states that play the causal/functional roles and have the internal structures that our mentalstate concepts describe. On this view, if Fido (for example) believes that the cat is up the tree, then he has in his brain an individual state, s, that plays the causal/functional role that beliefs play according to our folk psychology and has an internal structure similar to the “that”-clause that we use to specify its content (i.e., s has the structure Rxy, where “R” represents the two-place relation up, “x” represents the cat, and “y” represents the tree). Since the internal state s is seen as having an internal structure similar to the sentence “the cat is up the tree,” common-sense functionalism is often taken to support the view that thinking (including animal thinking) involves a language of thought (Fodor []). It is argued that since animal behavior is often successfully predicted and explained by our folk psychology, we have defeasible grounds for supposing that animals actually have such internal states in their heads. It has been questioned, however, whether our everyday ascriptions of thought to animals really do presuppose the existence of sentence-like representations in their heads (Stalnaker []) or whether there is any empirical evidence that shows that animals have and process such representations as opposed to iconic (non-sentential) representations. 



Some (e.g., Searle []) would take this to be a reductio of the intentional systems theory, but not Dan Dennett and John McCarthy: both claim that a “thermostat is one of the simplest, most rudimentary, least interesting systems that should be included in the class of believers” (Dennett [], p. ). For the latest on the instrumentalism versus realism debate regarding mental-state attributions to animals, see Jamieson (chapter ), Saidel (chapter ), Tetzlaff and Rey (chapter ), and Sober (chapter ); and for the latest on the sentential versus iconic debate regarding mental representations in animals, see Rescorla (chapter ), Tetzlaff and Rey (chapter ), Carruthers (chapter ), and Camp (chapter ).

The philosophy of animal minds: an introduction



In the past thirty years or so, due in large measure to the demise of radical behaviorism and the birth of cognitivism in psychology, as well as from the influential writings of Donald Griffin (, ) and Randy Gallistel (), scientists from various fields have found it increasingly useful to propose, test, and ultimately accept hypotheses about the causes of animal behavior in explicitly folk-psychological terms. According to the argument from science, since scientists are finding it useful to test and accept hypotheses about animal behavior in folk-psychological terms, we are justified in believing that animals have such states of mind. Not everyone, however, has found the argument from science convincing. The chief concern is whether explanations of animal behavior in folk-psychological terms are, as the argument assumes, scientifically respectable (see Wynne []; Kennedy []). It is sometimes argued that many (if not all) such attributions of mentality to animals violate a basic methodological tenet of the science of animal behavior called Lloyd Morgan’s canon: In no case may we interpret an action as the outcome of the exercise of a higher psychical faculty, if it can be interpreted as the outcome of the exercise of one which stands lower in the psychological scale. (Morgan [], p. )

Since animal behavior can always be explained in psychological or behavioral terms that do not appeal to our folk-psychological concepts, any scientific explanation of animal behavior in terms of such concepts is extravagant and scientifically unwarranted. It is also argued that scientific explanations of animal behavior are objective in that there is typically general agreement among researchers on what counts in favor of or against the explanation, which implies that, since the only generally agreed-upon indicators of consciousness are the verbal reports of subjects, explanations of animal behavior in terms of consciousness are unscientific (see Clayton et al. [], p. ). No contemporary philosopher is better known for his criticism of thought and reason in animals than Donald Davidson. In a series of articles (b, , ), Davidson put forward three interrelated arguments for his denial of thought and reason in animals. The first, known as the intensionality test, argues that our de dicto belief ascriptions to animals – that is, our belief ascriptions that aim to describe how the animal is thinking about some object in the world – are unwarranted, since without linguistic 

For the latest on the debate over the justification of Lloyd Morgan’s canon (and other principles of simplicity) used by scientists, see Sober (chapter ) and Fitzpatrick (chapter ); for the latest on the debate over the ascription of consciousness to animals, see Gennaro (chapter ), DeGrazia (chapter ), and Roberts (chapter ).



The philosophy of animal minds

behavior to appeal to, there are always countless different ways of saying how the animal is thinking and no principled method of deciding among them. Davidson’s second argument, the argument from holism, claims that we are also unwarranted in our de re belief ascriptions to animals – that is, our belief ascriptions that aim to identify the objects in the world the animal is thinking about independently of how it is thinking about them – since there are always countless different categories of objects about which the animal might be thinking and the only way to decide among them is to know what general background beliefs the animal holds, which, Davidson claims, is impossible without the animal being able to speak. Finally, Davidson’s main argument is that if animals really did have beliefs, then they should be subject to surprises on those occasions when their beliefs turn out to be false; but being so surprised, Davidson maintains, involves being aware that one’s former belief failed to fit the facts and thus requires having an idea of a world of objective facts. Davidson goes on to claim that the only way for a creature to come to have an idea of a world of objective facts is by comparing its own beliefs with those of others, which it cannot do, according to the intensionality test and the argument from holism, if it cannot interpret the speech of others – as animals, Davidson maintains, plainly cannot. The upshot is that only creatures capable of understanding speech can have beliefs. Detractors of Davidson’s arguments have tended to take one of three approaches. Some, such as Berm´udez (b), have sought to develop a theory of how to make principled de dicto ascriptions to animals; others, following Armstrong (), have sought to defend our de re ascriptions to animals by rejecting Davidson’s radical holism; and still others, such as Carruthers () and Tye (), have sought to undermine Davidson’s main argument by challenging its pivotal claim that surprise involves beliefs about beliefs. 5. the contemporary debate over animal consciousness Two general approaches to consciousness – the higher-order representational (HOR) approach and the first-order representational (FOR) approach – have played an important role in the philosophical debate over the status of animal consciousness. The dominant HOR approach has been the higherorder thought (HOT) theory of consciousness, according to which a mental 

See Jamieson (chapter ) for a critical commentary on Davidson’s arguments, as well as Lurz () for a general list of such critical commentary.

The philosophy of animal minds: an introduction



state is conscious just in case the subject has (or is disposed to have) the higher-order thought that he is in such a mental state (Carruthers []; Rosenthal []). The question of animal consciousness on this theory becomes the question of whether animals are capable of such higher-order thoughts. A common argument against higher-order thoughts in animals is that the possession of such thoughts, in virtue of the mental-state concepts they contain, entails certain linguistic or mindreading capabilities that animals appear to lack. Berm´udez (a, and chapter , this vol.), for example, argues that since animals are incapable of speaking and interpreting a public language, they cannot possess concepts of propositional attitudes (e.g., beliefs and desires) and, therefore, cannot have higher-order thoughts about their own or others’ thoughts. On different grounds, Carruthers () has argued that if animals really do possess mental-state concepts, then they ought to be able to apply these concepts to other animals for the purpose of anticipating and manipulating their behaviors – that is, they ought to be able to engage in mindreading. Carruthers goes on to argue, however, there are no incontestable empirical cases of mindreading in animals and some studies appear to show that not even the chimpanzee – the animal most likely to engage in mindreading – is capable of it (see, for example, Penn and Povinelli [b]; Povinelli []). Both of these arguments, however, have been challenged recently. Lurz () has raised a series of objections to Berm´udez’s argument against propositional attitude mindreading in animals, and Gennaro (chapter ) and DeGrazia (chapter ) argue that a number of recent studies of animal mindreading and metacognition strongly indicate that apes, monkeys, and dolphins are capable of higher-order thoughts about their own minds and the minds of other animals. In contrast to HOT theories of consciousness, FOR theories hold that mental states are conscious not because the subject is higher-order aware of having them but because the states themselves make the subject aware of the external environment (Dretske []; Tye []). Mental states that make subjects aware of the environment do so, according to FOR theories, in virtue of their having (or being poised to have) an effect on the subjects’ belief-forming system. FOR theorists argue that many varieties of animals, from fish to bees to chimpanzees, form beliefs about their environment based upon their perceptional states and bodily sensations and, therefore, enjoy conscious perceptual states and bodily sensations. 

The alternative HOR approach is the higher-order perception (HOP) theory of consciousness, defended by Lycan () and Armstrong (). See Lurz () for a description of the HOP theory and its place in the philosophical debate over animal consciousness.



The philosophy of animal minds

FOR theories are at their best, it has been argued, when explaining the consciousness of perceptual states and bodily sensations but have difficulty explaining the consciousness of beliefs and desires (Lurz []). Some FOR theorists have responded by endorsing a HOT theory for conscious belief and desire and a FOR theory for perceptual and sensory states (see Tye []; Dretske [], p. ). However, such a hybrid view would appear to belie the FOR theory’s claim to parsimony and antecedent plausibility regarding ascriptions of consciousness to many lower animals. For it seems rather reasonable to suppose that a perceptual state or bodily sensation would count as conscious only if it has an effect (or is poised to have an effect) on the subject’s conscious belief-forming system. But if that is so, then a creature will have conscious states on this hybrid view only if it is capable of forming higher-order thoughts about its own beliefs. Not only would such a view require higher-order thought for consciousness, as HOT theories do, it would be more demanding than HOT theories by requiring animals capable of conscious perceptual states and bodily sensations to have higher-order thoughts about their own beliefs. What appears to be needed here in order to save FOR theories from this problem is a nonhigher-order account of conscious belief (see Lurz [] for a sketch of such an account). 6. the roadmap to the volume The essays in this volume are organized around topics as well as whether their focus is metaphysical or epistemological. The first two essays by Jamieson (chapter ) and Saidel (chapter ) are on epistemic questions concerning our ascriptions of thought (e.g., belief and desire) to animals. Jamieson proposes to resolve a tension that he identifies as existing between the belief that animals think and the belief that what they think cannot be characterized. After rejecting various eliminativist and realist approaches, Jamieson concludes that interpretivism – the view, roughly, that whether and what animals think is deeply connected to whether we find it useful to attribute thoughts to them – offers the best resolution. Saidel, on the other hand, puts forward a realist argument for beliefs and desires in animals based on evolutionary and empirical considerations. He argues that since human beings satisfy the behavioral criterion for belief-desire attribution 

If scientists were to discover, for example, that the perceptual states involved in subliminal perception or blindsight actually caused subjects to form unconscious beliefs about their environment, none but the most committed FOR theorist would conclude from this alone that subliminal perception and blindsight were, after all, just cases of conscious perceptual awareness.

The philosophy of animal minds: an introduction



(i.e., goal-directed behavior), it is likely that many animals with which we share a close evolutionary past do too; he argues further that there are a number of scientific observations of goal-directed behaviors in monkeys, chimpanzees, and bonobos that are justifiably explained in terms of beliefs and desires. The next three essays are on the topic of the representational basis of animal thinking and reasoning (particularly about environmental matters). The question – which has both metaphysical and epistemic dimensions – is whether animals think and reason by means of sentence-like representations in a language of thought or by means of iconic representations, such as cognitive maps. Rescorla (chapter ) argues that even the most deductive-like reasoning in animals – such as the syllogistic reasoning famously depicted in the tale of Chrysippus’ dog – can be sufficiently explained without recourse to sentential representations. Rescorla illustrates the viability of this position by providing an explanation of the behavior of Chrysippus’ dog, as well as similar deductive-like behaviors in chimpanzees, in terms of Bayesian probability calculations over cognitive maps. Tetzlaff and Rey (chapter ), on the other hand, argue that the mental representations of honeybees that underlie their navigational and communicational abilities are systematic and, therefore, are best understood as sentential representations in a language of thought. Carruthers (chapter ) goes further and argues that not only do honeybees and invertebrates in general have compositionally structured mental representations, but their mental representations in some cases constitute genuine thoughts and concepts. Carruthers defends this position against the generality constraint on concept possession, showing that on its most plausible interpretation, the mental representations of invertebrates conform to it. The next two essays by Camp (chapter ) and McAninch, Goodrich, and Allen (chapter ) continue the discussion on the representational and conceptual abilities of animals by examining some recent empirical data and theoretical arguments on the cognitive and emotive natures of vocal communication in monkeys. Camp examines a recent argument by Cheney and Seyfarth () that the vocal communicative behaviors of baboons indicate that these monkeys think in a language of thought. Camp disagrees and argues that the communicative behavior of baboons can be explained in terms of their thinking in mental maps, diagrams, and other iconic representations. She argues further that one plausible difference between the baboon’s representational system and a genuinely linguistic one is that in baboons, the syntactic structures by which representational constituents are composed themselves still have a dedicated semantic function,



The philosophy of animal minds

whereas in a recognizably linguistic system, the compositional relation is sufficiently abstract or general to permit the composition of a wide range of distinct representational constituents. McAninch, Goodrich, and Allen re-examine a longstanding debate in cognitive ethology over whether animal communicative vocalizations are merely expressive or referential. The authors advance the debate by arguing that a neo-expressivist theory of avowals and ethical claims, in which an utterance is understood to have both expressive and referential functions, offers a new and better account of the data on the cognitive and emotive elements of animal calls. The next two essays, by Jos´e Luis Berm´udez (chapter ) and Jo¨elle Proust (chapter ), continue the debate over the representational basis of animal thought and thinking; however, their focus is on the question of what kinds of representational structures are necessary for thinking and reasoning about mental states, and whether animals are capable of thinking in such structures. Berm´udez draws an important distinction between minimal mindreading (i.e., the capacity to coordinate one’s behavior with another subject’s mental states) and two types of substantive mindreading – perceptual mindreading (i.e., the capacity to think and reason about the perceptual states of others) and propositional attitude mindreading (i.e., the capacity to think and reason about the propositional attitudes of others). He argues that since the latter type of mindreading requires the deployment of representational vehicles that are both propositional and accessible to consciousness, and arguably possible only through the possession of a public language, animals are incapable of propositional attitude mindreading – although they are capable of minimal mindreading and perceptual mindreading. Proust agrees with Berm´udez’s conclusion that animals are incapable of mindreading (or metarepresenting) their own and others’ propositional attitudes, but argues that they are, nevertheless, able to engage in metacognition (i.e., the ability to control and monitor one’s own cognition). After reviewing some of the recent studies on metacognition in animals, Proust shows that the data can be plausibly accommodated by a non-propositional, feature-based representational format. The next three essays, by Gennaro (chapter ), DeGrazia (chapter ), and Roberts (chapter ), examine various questions pertaining to the existence and the nature of consciousness, self-awareness, and emotions in animals, as well as questions pertaining to our epistemic grounds for ascribing such states to animals. Gennaro defends the existence of consciousness in animals according to the higher-order thought (HOT) theory of consciousness. He argues that a number of recent studies in metacognition,

The philosophy of animal minds: an introduction



episodic memory, and mindreading in animals persuasively show that many animals possess the kinds of higher-order thoughts required for consciousness according to the HOT theory. DeGrazia draws a useful distinction between different levels of self-awareness and argues that the cumulative force of various empirical findings on intentional behavior, episodic memory, imitation, mirror self-recognition, mindreading, and metacognition in animals shows that bodily self-awareness is quite widespread in the animal kingdom, and that social self-awareness and (even) introspective self-awareness are likely present in various higher species (e.g., monkeys, apes, and cetaceans). Roberts examines the existence and limits of animal emotions through the lens of his concern-based construal theory of emotion. According to this theory, emotions are a kind of perception that do not require thought and, therefore, are likely possessed by a wide range of animals. However, Roberts argues that emotions differ in sophistication according to a number of important dimensions, and that the level of sophistication of emotion in animals, as measured by these dimensions, is importantly lower compared with that of human beings. The last two essays, by Sober (chapter ) and Fitzpatrick (chapter ), return the volume to a set of epistemological concerns – in this case, regarding the scientific knowledge and practice of investigating animal minds. As noted in section  above, appeals to Lloyd Morgan’s canon (or the principle of conservatism or simplicity) are often used by scientists to justify or refute a particular hypothesis about animal behavior and cognition, and yet the content and justification of the principle has long been recognized as obscure. Sober uses model selection theory in statistics to clarify the content of the principle and provide it with a rational (albeit instrumental) basis, which he argues is consistent with a realist interpretation of beliefs and desire in animals. He goes on to show how, on model selection theory, a higher-order intentional model of animal behavior (as in Hare et al.’s [] experiment on chimpanzees) may turn out to be more parsimonious (and thereby more justified) than the competing lower-order intentional model of the behavior. Fitzpatrick examines the use of the principle of simplicity in the recent debate over mindreading versus behavior-reading in chimpanzees and argues that the principle’s use in the debate is best understood on a deflationary model, according to which a simpler theory is justified over a complex one, not on grounds of simplicity per se, but on the various background theoretical considerations in play. Fitzpatrick goes on to show that, on the deflationary model, the simplicity arguments (by Tomasello and Call []) that have been advanced in favor of the mindreading hypothesis and against the behavior-reading hypothesis (by

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The philosophy of animal minds

Povinelli and Vonk []) are justified and suggest exciting new avenues of future empirical research. The essays in this volume represent the current state of play in the field at its highest level. For this reason, I believe, the volume will be of value to both philosopher and scientific researcher alike. In addition, I am hopeful that the essays, by virtue of raising new and exciting ideas and reinvigorating important old debates, will come to serve as a welcome invitation and a useful springboard for future philosophical reflections on the nature, the existence, and our knowledge of animal minds.

chapter 1

What do animals think? Dale Jamieson

1. introduction You may have noticed that the title of this essay is ambiguous. Asking what animals think could be part of an inquiry into animal public opinion, focusing perhaps on such questions as what apes think about the Endangered Species Act or whether frogs prefer Tom Waits to Leonard Cohen. Or if you read the right punctuation into the title you might see it as an exclamation of surprise: “What! Do animals think?” What I am actually concerned with in this essay is how we should think about specifying exactly what it is that a particular animal thinks on a particular occasion. Some would say that I am concerned with the problem of content as it applies to non-human animals. The first response to the question of what animals think may be to say that they think thoughts. This is harmless, so long as we do not succumb to the temptation of reading psychology or ontology directly off of the language. However, it is downright harmful if, after assimilating thinking to having a thought, we go on to suppose that having a thought is the same as having a propositional attitude. At the outset, anyway, I want to leave open a wide range of possibilities including whether animal thinking implies having propositional attitudes; and if it does, the meaning, status, and nature of these propositional attitudes. For these reasons, unless I say otherwise, I will use expressions such as “thinking” and “having a thought” in neutral, common-sense ways, and I will take believing and desiring as examples of thinking, and beliefs and desires as examples of thoughts. Why should we be interested in what animals think? I am interested in this question because of how it relates to other questions. In earlier work I have argued that a plausible theory of value is one which distinguishes Thanks to Beatrice Longuenesse for her comments on an earlier draft, and especially Robert Lurz for his probing questions, not all of which I have been able to answer.





The philosophy of animal minds

things of primary value from things of derivative value. This is not a distinction in the degree or extent of value, but in the source of value. What is of primary value are those creatures who we take to be animate and sentient, and this includes many non-human as well as human animals. We owe moral duties to such creatures, but exactly which moral duties depends on the creature’s interests, and this in turn is associated with the character of the creature’s psychological life. In order to know what our duties to animals are, it is not enough to know that they think, we must also know something about what they think. An additional reason for being interested in what animals think is because it is unsatisfying to be told that we have good reasons for supposing that animals think even though we know very little about what they think. This is a little like being told that Sean is beautiful by someone who has never seen Sean. In both cases the claim could be both true and justified. There may be inductive reasons for the claim, a highly credible authority may testify that the claims are true, or the claims may follow from a wellconfirmed scientific theory. Nevertheless in the normal case we expect there to be an association between the general or abstract claim, and more specific claims that bear on it directly. When this is not the case it is somewhat disconcerting. For both of these reasons my question is important. Many of the considerations I adduce are not new, but they have not often been systematically brought to bear on the question that I am asking. Moreover, I believe that some of the most important lessons of the philosophy of mind of the second half of the last century have faded, and that there is some point in being reminded of them. As we shall see, answering my question turns out to be surprisingly difficult, and may lead us to question some conventional views about what we are doing when we attribute thoughts to human as well as non-human animals. Before beginning in earnest, I want to clarify the language that I will be using and confess to some simplifications. I will sometimes use “humans and animals” to mark the same distinction as that between human and nonhuman animals. I will also assume, unless I note otherwise, that humans are language-users and non-humans are not.   

Jamieson (), Essay . There are of course important questions here about the role of justification and correctness in these “takings,” some of which I discuss in Jamieson (), Essay . While there are analogies between these two cases there are also disanalogies. It is natural to suppose that Sean’s beauty supervenes on properties that are in principle observable, while the claim that animals think is a generalization from particular instances of animal thinking.

What do animals think?



2. the problem The fundamental problem we face is that most people are committed to the following pair of propositions: () Many animals think. () Exactly what animals think on particular occasions cannot reliably be characterized. There are many reasons that figure in the widespread endorsement of (). For many or most cases, when it comes to anticipating, explaining, and modifying their behavior, supposing that animals think works about as well as supposing that humans think (it seems equally natural to attribute to Grete the dog and Riki the infant the desire to play – perhaps with each other). Many animals behave in ways that are similar to humans, and there is also remarkable physiological and neurological similarity and continuity between us and at least many of them. It would be surprising, perhaps even the biological equivalent of the Immaculate Conception, if we were nature’s only minded creatures. A further consideration comes from evolutionary theory. Some influential accounts of the evolution of mind appeal to the sorts of environmental and social problems that our ancestors would have faced relating to the pressures of group living and the need to engage in cooperative hunting and foraging. Since the ancestors of many other animals faced similar problems in similar environments, it is plausible to suppose that the same evolutionary forces that selected minded human ancestors selected minded ancestors of other closely related animals as well. Finally, attributing mental states to animals is generally part of an outlook that recognizes them as morally significant. While most people are not animal rights activists, they do think it matters morally how animals are treated, and a commitment to () coheres with this commitment. While these considerations may or may not, singly or together, with or without further commitments, constitute a full justification for (), they are certainly important in explaining why the commitment to () is so ubiquitous. The ground of our belief in () is less obvious, but it is brought out clearly in the following passage from Stich (/). Suppose . . . that Fido is in hot pursuit of a squirrel . . . A moment later we see Fido craning his neck and barking excitedly at the foot of an oak tree . . . To  

I have in mind the work of Alison Jolly and Nicholas Humphrey. Their early papers are reprinted in Byrne and Whiten (). In Jamieson (, Essay ), I express skepticism about whether our commitment to the view that some animals are minded requires justification at all.

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The philosophy of animal minds

explain Fido’s behavior, it would be perfectly natural to say he believes that the squirrel is up in the oak tree. But suppose now that some skeptic challenges our claim by focusing our attention on the differences separating Fido’s belief from ours. “Does Fido really believe it is a squirrel up in the oak tree? Are there not indefinitely many logically possible creatures which are not squirrels but which Fido would treat indistinguishably from the way he treats real squirrels? Indeed, does he believe . . . that the thing in the tree is an animal ? Would it not be quite the same to Fido if he had been chasing some bit of squirrel-shaped and squirrelsmelling machinery . . . ? The concept of animal is tied to the distinction between living and nonliving, as well as to the distinction between animals and plants. But Fido has little grasp of these distinctions. How can you say that he believes it is a squirrel if he doesn’t even know that squirrels are animals?” (pp. –)

It is these sorts of considerations that lead to our endorsement of (). If we lose confidence in supposing that Fido believes that the squirrel is up in the oak tree, then there are going to be many cases in which we should lose confidence in supposing that we can reliably characterize what an animal thinks. What is true in this case generalizes to other cases. It is of course logically consistent to hold both () and (), but for reasons that I have already discussed it is uncomfortable to do so. For it is natural to wonder why you would hold () if you hold (). Suppose, for example, that I go on about how Grete is a highly sophisticated thinker, but when you ask what it is that she thinks, I reply that I haven’t the faintest idea. It is certainly reasonable for you, in light of my response, to wonder why I have such confidence in Grete’s cognitive powers. The problem that we face is not to evade some supposed inconsistency between () and (), but rather to resolve the unease that arises from the conjunction of our confident commitment to the view that animals think and our apparent ignorance about what it is that they think. 3. some responses In this section I discuss in turn four families of responses to our problem: eliminativism, wet eliminativism, the brute content view, and interpretivism. While none of these views can be rejected out of hand, I explain why I am most attracted to some version of interpretivism.



Davidson () adduces similar considerations. However, we should be cautious in over-generalizing. If we were to follow Geach, Kripke, and Ziff in supposing that proper names are purely referential devices, then these considerations would not count against Fido having such beliefs as this is Paula, when in the presence of his guardian.

What do animals think?



3.1. Eliminativism (reject (1)) Eliminativism, which rejects the claim that animals think, comes in more or less sophisticated versions. The most sophisticated version is Davidson’s; the least sophisticated version is the verificationism with which his view is sometimes confused. We’ll begin with verificationism. The verificationist argument asserts that the truth of () is grounds for giving up (). Roughly, if we cannot know what animals think, then we cannot know that they think. The argument can be reconstructed as a reductio ad absurdum in the following way. (i) Many animals think. (ii) Thinking entails thinking something in particular. (iii) Exactly what animals think cannot reliably be characterized. (iv) We cannot verify the truth of (i). (v) We should only believe what we can verify. (vi) We should not believe (i). (i) is the claim that is under consideration. (ii) might be regarded as analytic. (iii) is supported by the sort of considerations adduced by Stich and quoted in section . (iv) might be thought to follow from (iii), based on some such principle as “no verification without reliable specification.” (v) is the statement of the verification principle, and (vi) draws the conclusion from (iv) and (v). It should be clear that this argument is only as good as the verification principle and the principle that licenses the inference from (iii) to (iv). These principles are implausible because they prove too much: they imply that we should not believe some truths which we surely should believe. Consider, for example, the following case, which appears to be analogous to () and (): () Black holes exist. () Exactly what black holes are like cannot reliably be characterized. () may well be true, but this does not seem to require us to give up (). We may believe that black holes exist because we know something about the causal roles that they play (e.g., that their gravitational force affects neighboring bodies), even if we can say little about them in other respects. 

For Davidson’s view see his (a), Essay ; (); and (), Essays  and . In the text I discuss a weak version of verificationism, which holds that we should only believe what we can verify, rather than a strong version such as the one espoused by the logical positivists, which holds (roughly) that what cannot be verified is meaningless. I discuss the weak version because it is more likely to be thought plausible.

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The philosophy of animal minds

It may be replied that knowing the causal roles that black holes play constitutes reliably characterizing them, but this is not plausible. I may know that it was Kelly who broke up my marriage, but if I know nothing else about Kelly (even Kelly’s gender), then surely I have not reliably characterized Kelly when I say that Kelley is the person (?) who broke up my marriage, even though I certainly know that Kelly exists. But suppose that we were to agree that in specifying Kelly’s causal role (at least with respect to my marriage) I have reliably characterized Kelly, and thus Kelly’s existence has been verified. If we were to say this, then it seems just as plausible to say that animal thinking is also verified since it seems plausible to say that on particular occasions we know the causal role of what an animal thinks in producing the animal’s behavior (think, for example, of Fido). If knowing something’s causal role is sufficient for reliably characterizing it, then we can reliably characterize what an animal thinks on many particular occasions, and thus, on this view, we are entitled to say that animals think. Whichever way we go on the question of whether knowing something’s causal role is sufficient for reliably characterizing the thing in question has unwelcome consequences for the eliminativist. For the eliminativist claims that () (with some further assumptions) is sufficient for rejecting (), but my arguments show that either () fails to be sufficient for rejecting (), or many other important beliefs will have to be rejected as well. There is of course more to say. For example, an eliminativist could attempt to sever the analogy between () and (), and () and (), perhaps because the logic of the sentences is not analogous or because thoughts are not like physical objects. However, given that verificationism is generally down on its luck, I will put these possible rejoinders aside and move on to other views that might be thought to be more plausible. Davidson’s version of eliminativism is more sophisticated. While his arguments can be reconstructed in a number of different ways, this gets to the heart of what he is asserting: (i) In order to think, a creature must have a full range of propositional attitudes. (ii) Having a full range of propositional attitudes rests on having language. 

Some may reject the analogy between Kelly and black holes because Kelly is an individual and black holes are kinds. While I don’t believe that this dissimilarity matters in this case, I think the same sort of intuitions that I am attempting to mobilize here arise with respect to other examples which involve kinds (e.g., I doubt that we would say that we can reliably characterize a kind of artifact about which we only know that it is a kind and that it triggered an explosion in human cultural evolution).

What do animals think?



(iii) Animals do not have language. (iv) Therefore, animals do not have a full range of propositional attitudes. (v) Therefore animals do not think. The intuitive core of Davidson’s argument is that ascribing a single belief to something requires ascribing “endless” further beliefs to it (as we saw in the Fido case), and having “endless” further beliefs requires such a sophisticated ability to make discriminations that only language is complex enough to do the trick. Premise (i), which expresses Davidson’s commitment to holism, is controversial, but I will not challenge it here. Instead I will focus on premise (ii). On the face of it, (ii) is not very plausible. It does not seem difficult to imagine languageless creatures who have propositional attitudes. Nevertheless, Davidson argues that this is wrong. Languageless creatures do not have propositional attitudes because (a) language is required for a belief to have content and identity; and (b) one must be able to have beliefs about beliefs in order to have any beliefs at all, and having beliefs about beliefs requires language. There are at least two reasons why the first argument (a) for supposing that (ii) is correct seems implausible. First, while it may be plausible to suppose that having a representational system is necessary for a belief to have content and identity, it is not obvious that the representational system must constitute a language. Davidson has arguments that are supposed to show that only a language has the associative and expressive power required for having propositional attitudes, but again these are controversial. Moreover, even if it is true that propositional attitudes require a representational system with the associative and expressive power of a language, why must this be a language that is deployed publicly and interactively rather than occurring simply as a language of thought? It would be consistent with this view to suppose that many animals have languages of thought that play a role in determining the content of their thinking, but that these thoughts are not expressed publicly in language. Finally, and more controversially, even if we suppose that language is required for an organism to have propositional attitudes, why must it be the organism rather than the organism’s interpreter who is the language-user? On this view (related to the interpretivism that I will discuss in section .), propositional attitudes can be thought of as co-creations between the organisms 

This is basically Fodor’s view in (), though he also rejects most of Davidson’s other arguments as well. This view will be discussed further in section ..



The philosophy of animal minds

to whom they are attributed and the language-users who attribute them. Davidson’s second argument (b) for supposing that having propositional attitudes rests on having a language is also implausible. This argument supposes that one must be able to have beliefs about beliefs in order to have any beliefs at all, and that having beliefs about beliefs requires language. Davidson’s idea is that if I believe that P, then I would be surprised to discover that not P. If I am surprised to discover that not P, then I come to believe that my original belief was false. This involves having the secondorder belief that my first-order belief was false, which involves having the concept of belief. One can question almost every claim and step in the argument. On the face of it, it seems implausible to suppose that having a first-order belief requires having a second-order belief, and that having such a secondorder belief is required for surprise. Animals often behave in ways that are described both by ordinary people and scientists as surprised, and this doesn’t seem to require second-order beliefs. It is easy to imagine Grete expressing surprise that the bone which she previously buried in some particular place is not there when she returns to dig it up. She expresses her surprise through barking, running in circles, general agitation, looking up at her guardian, and so forth. While it seems plausible to say in such a case that Grete is surprised that her bone is not there, this attribution does not seem to make essential reference to Grete forming a second-order belief that her first-order belief about the location of the bone was false. Attributing surprise to Grete in this case seems to make reference only to her behavior, expectations, and state of the world. Even if we supposed that first-order beliefs do require second-order beliefs, it would seem that some animals satisfy this criterion. Although I cannot pursue this point here, it has been persuasively argued that deception, play, and self-recognition (attributed to various apes, monkeys, canids, and cetaceans) all involve second-order beliefs. In order for Davidson’s view to be convincing, it must be shown that several options that on their face seem plausible are not really options at all. Most of us would want to distinguish the following: Grete having a belief that we don’t know how to interpret, Grete having a belief about bones that is different from our belief about bones, and Grete and us sharing a belief

 

See, e.g., Tinkelpaugh (). See, e.g., Allen and Bekoff (), Tomasello et al. (), and Whiten and Byrne ().

What do animals think?



about bones. However, if Davidson is right, these distinctions collapse: they are not viable, at least as ordinarily understood. Eliminativism is a view that is implausible on the face of it, and embracing it requires accepting implausible philosophical doctrines such as verificationism, or arguments that are very difficult to defend. It is not surprising that, except for some hold-outs, eliminativism has been in retreat since the seventeenth century. 3.2. Wet Eliminativism (weaken (1)) A second approach is to embrace a weaker form of eliminativism. Rather than rejecting () outright, this view accepts () in some weaker form while rejecting it in another stronger form. As Stich concludes in an earlier article (, p. ): “Do animals have beliefs? To paraphrase my young son: ‘A little bit they do. And a little bit they don’t.’” The idea here is that something belief-like goes on with many animals and that is why we find it natural to say that they have beliefs: they behave in a goal-directed way, they discriminate between various stimuli, and so on. But the fact that these apparent beliefs cannot be reliably characterized indicates that they are not beliefs in the same sense in which humans have beliefs. Hence animals “a little bit” have beliefs and “a little bit” do not, and animal thinking is “a little bit” eliminated and “a little bit” not. Wet eliminativism seems ambiguous between two general views. On one of these views, having a belief comes in degrees. Humans and other animals both have beliefs, but humans have beliefs to a greater extent than other animals. On the second general view it is only humans that have beliefs. Animals have states that are similar to beliefs in some respects, but they are not beliefs at all. The first of these two general views of wet eliminativism is itself ambiguous between two readings: is it the having of beliefs that comes in degrees, or is it the beliefs that are matters of degree? The first of these readings borders on the unintelligible. Suppose that we are untroubled about saying that both Grete and I believe that Paula is at the door. It would then be strange to suppose that while Grete and I have a belief in common, I have it to a greater extent than she does. Of course, I may hold the belief more strongly than Grete does (or vice versa); there is no mystery about degrees 

The discussion of Davidson reprises and revises some material in Bekoff and Jamieson (). Indeed, many of the core ideas of this essay were first mooted there and in Jamieson (, Essay ).

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The philosophy of animal minds

of belief in this sense. We may even assign a metric (perhaps related to the probability calculus) and say that my degree of belief is . while Grete’s is .. However, it doesn’t follow from our having different degrees of belief in this sense that our having of the belief we have comes in degrees. On the second reading of this view it is the belief (rather than the having) that comes in degrees. On this reading both Grete and I have the belief that Paula is at the door in the same sense, but my belief is more of a belief than Grete’s belief. While it may seem plausible to suppose that Grete’s belief that Paula is at the door is in some ways different from my belief that Paula is at the door, it is not easy to see how such differences would be mapped onto a scale such that my belief is more of a belief than Grete’s belief. In any case it seems strange to say that while Grete and I share a belief, my belief is more of a belief than her belief. The second general view that a wet eliminativist may hold is that although animals do not have beliefs, they have states that are similar to beliefs in some respects. While this view may be plausible it cries out for development. The challenge is first to identify the respects in which animal states are similar to human beliefs, and then show why these are not sufficient for classifying the animal states as beliefs. Since this goes against the widely shared intuition that humans and animals have beliefs in the same sense (if not exactly the same beliefs), the wet eliminativist has a serious burden of proof to discharge in this case. There is a third general view that a wet eliminativist could hold. On this view animals have states that are similar to the states that we have when we have beliefs, but whether or not a state counts as beliefs on a particular occasion depends on how similar it is to a belief state that we would be in on such an occasion. So, for example, it is true that Grete believes that Paula is at the door on a particular occasion on condition that Grete is in a state sufficiently similar to the state that I am in when I too believe that Paula is at the door. This view moves away from eliminativism by sometimes embracing (), and on those occasions generally rejecting (). An obvious problem for this view is to provide a well-motivated account of what makes an animal state sufficiently similar to a human belief state on a particular occasion to count it as a belief. One response is to say that similarity on an occasion is determined by context. For example, if we’re interested in explaining Grete’s behavior, then we might say that our states are similar enough on this occasion for it to be true that Grete believes that Paula is at the door. If on the other hand the context is one in which we are interrogating Grete’s entire “worldview,” then on this occasion the 

See Jeffrey ().

What do animals think?



similarities would not be sufficient for it to be true to say that Grete has this belief (or presumably, any beliefs at all). Something has gone badly wrong. Context may be powerful enough to affect how to specify a belief correctly (as we will see in section .), but it is not powerful enough to transform a non-belief state into a belief, or a non-believer into a believer (and presumably back again). Either Grete’s state is a belief or it is not and either she is a believer or she is not, whatever wooly (or non-wooly) questions I may be asking. What has gone wrong is supposing that attributing beliefs to animals is really a disguised way of comparing their states to ours. It is not. Suppose that we weren’t around, either because we had never evolved, or because we had decided to take a permanent vacation from the planet. What, then, would we say about Grete’s states? Whether an organism has beliefs depends on the organism and its environment, not on the relations between its states and ours. In this discussion of wet eliminativism, I have followed Stich in focusing on belief as an instance of thought. To some extent our intuitions are affected if we focus on thinking rather than thought, and on specific forms of thinking rather than thinking in general. For example, it may seem more plausible to suppose that thinking comes in degrees rather than that specific forms of thinking (such as believing or desiring) come in degrees. Humans think to a greater extent than animals, it might be supposed, because humans engage in more types of thought (including higher-order thought) than other animals. However, even if this is plausible it would not imply that with respect to a particular form of thinking that is shared by humans and other animals (e.g., believing or desiring) humans have this form of thinking to a greater extent than other animals, much less that it would be the only form of thinking worthy of the name. Thus, this view is consistent with the full-throated endorsement of (). Nor would this view, even if true, help to explain why () seems false with respect to humans but true with respect to animals. However intuitive wet eliminativism may appear, it seems to do little to resolve the tension between () and (). 3.3. The brute content view (weaken (2)) The brute content view resolves the tension between () and () by weakening (), and claiming that in principle if not in practice we can reliably characterize what animals think on particular occasions. On this view there 

Of course, answers can be given, and philosophers being what they are, counterfactuals loom. When the question of whether a Springer Spaniel believes that Paula is at the door is supposed to turn on heavy-duty metaphysics, you know that something has really gone badly wrong.



The philosophy of animal minds

is some fact of the matter about exactly what animals think on particular occasions, though empirical circumstances or general difficulties about knowledge of other minds may stand in the way of our knowing exactly what it is. Such considerations may help to explain why many people find () plausible. However, despite difficulties that may arise in practice, in principle we can reliably characterize what animals think. One version of the brute content view is the brain-writing view. On this view there is a “language of thought” or some other system of mental representation encoded in the nervous systems of all those creatures (human and non-human) who have thoughts. There are different theories about how nervous systems encode representations and about the exact structure of the system of mental representation that the nervous system instantiates. What these views share is the idea that an animal’s brain is (metaphorically speaking) a text. If we cannot on a particular occasion reliably characterize what an animal thinks it is only because we cannot (yet) read the text. The information is there, in the animal’s brain, and in principle can be reliably characterized. A second version of the brute content view is one that I shall call Fregean. Like the brain-writing view this view holds that there is some fact of the matter about what an animal is thinking on a particular occasion that can, in principle, be reliably characterized. Unlike the brain-writing view, which holds that what an animal thinks can in principle be read off from the animal’s brain, the Fregean view holds that what an animal thinks can only be approached through the animal’s mind. While both views could be materialist in their ultimate ontologies, their proximate ontologies are different, as are their views about how we can come to know what an organism is thinking. Returning to Stich’s example of Fido helps us to see what these views have in common, how they are different, and how they attempt to resolve the tension between () and (). When we last checked in, Stich was puzzled about what exactly Fido believed when, after chasing a squirrel up an oak tree, he sat at the foot of the tree barking. After posing some apparently unanswerable questions, Stich’s puzzlement turned to skepticism about whether there was any fact of the matter about what exactly Fido believed. The mistake enters, according to the brute content view, when puzzlement   

This term is drawn from Dennett (). I am greatly indebted to his discussion there and elsewhere. The term “language of thought,” as well as the brain-writing view itself, was brought to prominence by Fodor (). This may be an unhappy name for this view. I call it thus because it can be seen as a version of a view in which sense determines reference.

What do animals think?



turns to skepticism. There is some fact of the matter about what Fido believes, even if Stich on this occasion does not know what it is. On the brain-writing view, the belief is constituted by mental representations in Fido’s brain that in principle could be read off if we were smart enough to do so and had the proper equipment. According to the Fregean view, our ignorance may be more immediately intractable (new, shinier equipment will not help), but nevertheless there is some fact of the matter about what Fido believes that is present in Fido’s mind, which we can discover in much the same way in which we discover what is present in the minds of humans. Since introspection is the epistemological gold standard for such views, in practice it may be difficult for us to know what Fido is thinking, but in practice it may also be difficult for us to know what our boss is thinking on a particular occasion. However, in principle in both cases the contents of thought can be reliably characterized, using roughly the same means. Thus the relation between () and () seems to be basically the same in both humans and other animals. There are several reasons why people find brute content views plausible. First, standard realist views in most domains hold that there can be a robust distinction between what is the case and what we are currently in a position to know. And whatever discomfort there may be between holding () and (), if we have good general reasons for supposing that animals think, then the fact that in practice our access may be poor on particular occasions is no reason to give up our belief that they think, especially if we have reason to think that in principle their thought can be reliably characterized. Second, many people seem to accept a particular model of our knowledge of other minds that may help to explain the residual differences between our knowledge of other human minds as opposed to our knowledge of animal minds. On this model people tell us what they think, while we infer the thoughts of animals and other languageless creatures. The fact that we can make these inferences about animals is grounds for weakening (), even though we are better at grasping the content of human minds than we are with animal minds. Finally, in our own case we have a strong intuition that part of what it is for us to be minded is that we can determine the 



Of course, the striking difference between humans and other animals that may be thought to bear on our knowledge of what they are thinking is that humans are language-users and other animals are not. Different brute-content theorists attach different degrees of importance and kinds of significance to this difference. I discuss this further in the next paragraph and in section .. See also Jamieson (), Essay . Animals discriminate among stimuli, and it is plausible to suppose that this provides a basis for at least some rough and ready content attribution. For work in this tradition, see Allen and Bekoff () and other papers by Colin Allen.



The philosophy of animal minds

contents of our own thoughts at least on some occasions, and it seems plausible to many people that if we can do this maybe Fido can do it too. Consider an example. Suppose that I tell you that I am thinking of a number between  and  and I ask you to guess what the number is. You don’t reject the question out of hand on the grounds that I can’t actually perform the task of thinking of such a number. We both think that I can determine the content of my thought in such a way that I can think of a number and you can be right or wrong when you guess which number it is. In this case there seems to be some brute content about what I am thinking. Either I am thinking of, say,  or I am not. If this is true for me about numbers, why can’t Fido’s thinking about a bone be like this as well? Of course, Fido does not think what a yummy bone! in English (or Latin for that matter), but the intuition here is that there is something that Fido is thinking, and insofar as he is a good phenomenologist he knows what it is while we are only guessing. It is difficult to argue against this view definitively for several reasons. First, it would require proving a negative existential (i.e., that this sort of thinking is not going on in Fido’s mind) and this is notoriously difficult to do. Second, this view is an expression of deep-seated Cartesian intuitions that, for whatever reason, never seem to go away. Finally, it is just plain difficult to deny that the sort of thinking described in the “choose a number” game actually goes on. But this apparent fact raises as many questions as it answers. How characteristic is this form of thinking? What is its relationship to other forms of thought? Are there other explanations (or descriptions) available for what is going on in this game? In what follows I will present some reasons for supposing that the brute content view is implausible, at least in comparison to the view that I will sketch in the next section, but I admit that nothing I say forces one to come down on one side of the question or the other. But then, that is hardly unusual in philosophical argument. There are various technical problems in formulating brute content views, but for many people the greatest obstacle to accepting them concerns the way in which they construe knowledge of our own minds and the minds of others. On the brain-writing version, the best way in principle to find out what someone is thinking is to look at the state of their nervous system. On the Fregean view, the best way in principle to find out what someone is thinking is to have access to what they introspect. On both views behavior provides, at best, derivative information that may support inferences regarding the representational state that the person is tokening or the mental state that the person is in. But this is hard to accept.

What do animals think?



Imagine that someone is in the presence of a dog. The person is grimacing, recoiling, and trembling. His blood pressure, respiration, and pulse are all elevated. It is difficult to imagine that we would give up our claim that the person was afraid of the dog simply on the basis of his denial, or even for that matter on the basis of a brain scan that proposed to show that what was inscribed in the person’s brain was that he liked the dog and wanted to pet her. In this case the relation between the behavior and the mental state seems as least as intimate as the relation between the mental state and the first-person report, or the mental state and the brain scan. What this brings out is how Cartesian both versions of the brute content view are in placing thought in the mind or in the brain. Indeed, on the Fregean view, the mind has many of the usual Cartesian properties (such as transparency). It is thus not surprising that such views have difficulty in acknowledging that our actual attributions of mental states are responsive to a wide range of considerations. There are some broader philosophical concerns that one may have with brute content views. Such views seem to rely on some idea of intrinsic representation, which is far from easy to characterize and make plausible. Presumably such representations are either irreducible or somehow derive from non-representational sources. While there is lively philosophical discussion about these alternatives, it is worth pointing out that our characteristic experiences are of representational systems such as language in which rather than being intrinsic, representations are interpreter-relative (e.g., rather than being intrinsic the meaning of “chair” is relative to interpreters: thus English-speakers normally use it to refer to chairs and French-speakers to flesh). Indeed, in our discussion of fear in the previous paragraph we were getting close to discussing the broader question of whether content attribution is interpreter-relative. While I cannot hope to settle the issue here, these remarks may set the stage for discussing what we might call an interpretivist approach to our question. 3.4. Interpretivism (reject (2)) Interpretivism begins from a view about why we are interested in knowing what a human or animal is thinking on a particular occasion. The 

 

Not all of these responses may count as behaviors, but what they have in common is that they are not the sort of data that the brute content view takes as bearing directly (as opposed to inferentially) on being in a particular psychological state. For some skepticism about such views see Dennett () and Clark (). The same is also true (of course) with sounds. Thus the sound associated with “dude” in English is normally used to refer to dudes by English-speakers and milk by Nepali-speakers. I owe these examples to Raquelle Stiefler and Anne Rademacher.



The philosophy of animal minds

interpretivist’s answer, in varying degrees of complexity and sophistication, is that we’re interested in making others intelligible and in living with them productively. If this is what we’re up to, then all sorts of things will bear on our attributions: background knowledge, appreciation of context, specific information about the human or animal, familiarity with his or her way of life, and general knowledge about the relation between mental states and behavior. On this view attributions may be true or false, but the truth-makers will be connected to some idea of success. In short, content attributions are one way among others of making ourselves and others intelligible; they aren’t just about corresponding to what’s in the mind or brain, they have important pragmatic dimensions as well. The core of interpretivism is the idea that there is a deep connection between what an organism thinks and what thoughts an interpreter would attribute to the organism. This characterization is both vague and rough, and there are many complications that I will put aside here, including these: What is the nature of the “deep connection”? Is it only organisms that can think? What are the relevant characteristics of the interpreter? What are the standards of correctness that apply to the interpreter’s attributions? Three features of interpretivism are especially important for our purposes. The first is the contrast between interpretivism and the brute content view. Rather than content being written in the brain or dancing before the mind’s eye, it is the product of an interaction between an organism and an interpreter. Second, while much of our thinking about the mind privileges the first-person point of view, interpretivism privileges other points of view. It is from the second- or third-person perspective that we answer questions about what an animal is thinking on a particular occasion. Finally, interpretivism resolves the tension between () and () in a way that is so simple that it will strike some people as a cheat. Since our reliably characterizing what an animal thinks on an occasion is “deeply connected” with supposing that it is minded, accepting () implies rejecting () (on at least most plausible interpretations). It is worth distinguishing two different strands of interpretivism, although I will not go into detail about them. Dennett’s interpretivism is an expression of empiricist skepticism about “inner” entities such as content and qualia. He admits to beginning with a “tactical” choice, declaring his “starting point to be the objective, materialistic, third-person world  

I have been helped here by discussions with Mihailis Diamantis. See Byrne () and Child () for good discussions of interpretivism (though, unfortunately, they do not agree on the name of the view, much less on how best to characterize it). For the views of the godfather of interpretivism, see Quine ().

What do animals think?



of the physical sciences.” He doesn’t claim to refute those who take the first-person perspective as the starting point, simply saying that “we beg the question against each other.” Nevertheless Dennett does not think his choice is arbitrary: “we can see more and better if we start here.” Davidson on the other hand is a transcendental philosopher. He sees some sort of a priori connection between the content of a thinker’s thoughts and an interpreter’s attributions. His interpreter is not Dennett’s happy-go-lucky guy in thrall to science, but rather what Davidson calls a “fully informed” interpreter. While Dennett’s interpreter is scientific and in principle willing to give up folk psychology at the end of neuroscience, Davidson thinks there are embedded norms and anomalies in our understandings of ourselves and others that are resistant to scientific reduction. Here, my interest in interpretivism is in how it resolves the tension between () and () rather than in the details of any particular account. For an interpretivist, the question of whether an animal thinks is deeply connected to the question of whether we can attribute thoughts to the animal on particular occasions. An interpretivist thinks we can. The question is how. Ideally, we would know all of the discriminations an animal would make on all possible stimuli. We would also know whether variances in response were due to discriminations on stimuli, or changes in state through time or circumstance. Of course, we could never know all of this and even if we did, there would still be indeterminacy about what particular state to attribute to the animal. For example, Grete’s behavior of walking to the door can variously be explained by mutually adjusting beliefs and desires. If we fix a desire, for example that Grete wants to urinate, then we can specify a belief (e.g., that on the other side of the door is a place in which it is appropriate for her to urinate) that will make the behavior intelligible. But if we fix a different desire (for example that Grete wants to play with Jethro), then we will have to adjust Grete’s beliefs accordingly in order to explain the behavior. This story about the interactions between contents and attitudes ramifies. While not anything goes, attributions answer to various pragmatic concerns, including those involving other attributions, and not only to what is known about the organism’s body, behavior, and  

Dennett (), pp. –. Davidson (/), p. . It may seem surprising to enlist Davidson on the side of those who endorse (), especially in light of the discussion of his eliminativist views in section .. Philosophers often have inconsistent strands in their thought, or fail to follow where their ideas lead. I agree with Jeffrey () that Davidson fails to draw the correct conclusions about animal thought from his interpretivist view. For further discussion, see also Jamieson (), Essay .



The philosophy of animal minds

the world. The indeterminacy is regimented on the basis of our purposes, interests, and other attributions, but it will not all go away. We can never wring out all the indeterminacies in explanation and attribution. Other attributions would be possible, and there would remain questions that we would not be able to answer. A crucial experiment about what Grete wants or believes would never eliminate all competing explanations. A decisive moment is, to some extent, in the mind of the beholder. In short, attributions are not uniquely determined. We can see what this comes to by returning to Stich’s Fido. Does Fido believe that the squirrel is up in the oak tree? Sure. That’s a good enough answer for most purposes. Of course, more questions can be asked, and in some cases answers can be given but in others they cannot. Attributions can be more or less finely honed and precisely shaped, but for most purposes the answer that I gave at the outset (and Stich came to doubt) is good enough. This may not satisfy everyone, but I think we should just get used to it. I don’t think there is much difference in asking this question about Fido and asking it about a person who has chased the squirrel up the oak tree. Does this person believe that the squirrel is up in the oak tree? Sure. Why wouldn’t he? There are many questions that we could ask about this person’s beliefs and the answers would be different depending on whether he is a Druid, a creationist, an arbologist, or just an ordinary guy who likes to chase squirrels (?!). There will also be questions without answers. But this may take us back to language. You might say the difference between the man and Fido is that we can ask the man whether he believes that the squirrel is up in the oak tree and he can tell us. Assuming he is not linguistically or audiologically impaired, yes he can tell us. Of course, the man may, like Fido, just look at us quizzically when we ask the question. Or he may instead utter some words. Whatever he does, whether it involves words or not, is just more behavior requiring further interpretation, subject to the same sorts of indeterminacies I have already discussed. What many may find most disturbing about interpretivism is that the first-person point of view seems to drop out. Have we left behind the idea that there is something that it is like to be Fido? What can an interpretivist say about the “choose a number” game? The first-person point of view need not fall away. We interpret ourselves as well as others. What was I thinking when I agreed to go to Manitoba in February? Do I really care about Sean as much as it seems? Why do I always find myself behaving this way around Kelly? What may fall away is the sort of first-person privilege that some philosophers seem to think we

What do animals think?



have. It may be replaced by a view in which we see ourselves as corrigible, self-interpreting creatures who sometimes know the minds of others better than we know our own, and sometimes are known better by others than we know ourselves. We may come to see ourselves as relentless story-tellers, constructing and reconstructing narratives in a constant attempt to make sense of ourselves and others, in a world in which our lives are thoroughly enmeshed with the material and social realities that govern our existence. Whatever is true about Fido, he is not a relentless story-teller, constantly engaged in the project of trying to make sense of himself. What, if anything, follows from this? Less than one might think. Perhaps, Fido’s first-person privilege is even thinner than yours or mine (should he or anyone else care). What certainly would not follow is that there is nothing that it is like to be Fido, or that Fido lacks a tendency towards coherence in action and belief. Indeed, a rich literature in animal behavior shows how many creatures revise their behavior in light of experience and novel circumstances. But what can an interpretivist say about the really hard case, the “choose a number” game? There are two ways an interpretivist might approach what is going on here. One way would be to weaken interpretivism and allow that there are some thoughts that are not subject to the interpretivist account as I have developed it. The trick, on this approach, is to quarantine these thoughts; try to show that rather than being central cases of thinking, they are in fact derivative: nothing more than some aimless spinning on the part of creatures who for a few moments have some respite from the struggle for survival and have nothing better to do. It would not be surprising, after all, if some kinds of thoughts are endemic to particular creatures. Perhaps creatures with language can have thoughts that languageless creatures cannot, just as creatures with sonar may be able to have thoughts that creatures without sonar cannot. The “choose a number” game is closed to rats and infants just as the NBA is off-limits to those under six foot six. However, this should not lead us to think that some special importance attaches to either. Just as we may play the “choose a number” game when we have nothing better to do (or when we are doing philosophy), so humpback whales may privately entertain the songs that they might like to sing. What would be a mistake is to take these peculiarities as central to thinking, or even perhaps as central to the thinking of the creatures who have them. On this view some idea of a “subjective” perspective may persist, but the idea that it has much freedom in fixing content or is at all paradigmatic of 

For references, and a defense of this claim, see Hurley ().



The philosophy of animal minds

thinking may fade. We may come to see endemic phenomenology as rare and indeterminate, at least with respect to a wide range of attributions. The second approach is perhaps more elegant but even less plausible. This approach imagines a time in which the first-person point of view has disappeared. People continue to self-report, but they no longer see such information as substantially different in kind from other reports about the world. Once our intuitions about the “choose a number” game are exposed to the clarifying solvent of advanced neuroscience and philosophy, they begin to cloud, and then disappear. Interpretivism in any version is not a view that is immediately plausible to everyone. However, it is a way of resolving the tension between () and (), and there is a case for it being more plausible than the other alternatives that we have canvassed. In the end, philosophical consistency always does seem to exact a price. 4. concluding remarks: content revisited In this essay I have identified a tension in our thinking about animal minds and reviewed some ways of trying to resolve it. If we are tempted by interpretivism, or find none of the approaches plausible, then perhaps we should rethink, not just the problem with which we began, but the very way in which we have come to portray the mind in philosophical discourse. For the most part I have avoided the word “content” in this essay, yet in the philosopher’s sense of the term it is the problem of content that I have been addressing. But what exactly is the problem of content? What is content anyway? The idea of content is a metaphor that infuses the way in which philosophers think about mind and language. Like all metaphors, it drags along its own presuppositions and associations like uninvited houseguests. In my opinion, we have not done enough to subject this metaphor to scrutiny, and, as it might be said, the unexamined metaphor is not worth deploying. One thing we can say for sure is that whatever mental content is like, it is not like the contents of a can of tomatoes. So with that remark I end – along with the advice that if you don’t like any of the answers to my question that are on offer, perhaps you should find some new metaphors.  

Though I do think that it comports well with at least some emerging scientific views of the mind – see, e.g., Edelman and Tononi (). The only person I know who has written about the metaphorical nature of the problem of content is Eric Schwitzgebel. His observations are available on the web at http://www.faculty.ucr.edu/ ∼eschwitz/SchwitzPapers/Containers.pdf.

chapter 2

Attributing mental representations to animals Eric Saidel

1. introduction We quite naturally attribute mental representations in order to explain actions. The cat is scratching at the door because he wants to come in the house and believes that scratching at the door will get him into the house. The dog is following me because she wants some of my food and believes that by following me she can get some of my food. Some of our attributions of mental representations are without doubt fanciful (does my car really not like to start on cold mornings?), but some of these attributions are accurate. For example, some, if perhaps not all, of the actions of adult human beings are properly explained by their desires and their beliefs about how to achieve those desires. What about the behavior of (nonhuman) animals? Are belief-desire explanations the right explanations of their actions? I argue that some (non-human) animal behavior is properly so explained, and thus that some animals truly have beliefs and desires. There are two strands of evidence which separately support this conclusion. First, behavior that is appropriately explained in terms of mental states such as beliefs and desires is behavior directed at a goal relative to which the agent is able to learn; and since human behavior meets this criterion, I argue, we should expect, on evolutionary grounds, that some animal behavior meets this criterion as well. Second, I show that a number of different scientific observations of animal behavior strongly support the hypothesis that animals engage in goal-directed behavior, behavior that is organized around a goal with respect to which they are able to learn and, hence, behavior that is justifiably explained in terms of their having beliefs and desires.

A version of this essay was presented to the faculty of the philosophy department at George Washington University. I am indebted to them, especially to David DeGrazia and Tad Zawidzki, for their helpful comments. I am also indebted to Robert Lurz for his detailed comments on a draft of this essay.





The philosophy of animal minds 2. the implications of causal realism

When an organism behaves in a way that is caused and structured by its goals, we naturally say of the organism that it wants to achieve such and such a goal and that it believes that the way it is behaving will enable it to achieve that goal. In this essay I assume a realist position regarding beliefdesire explanation: belief-desire explanations, if accurate, entail that the organism whose behavior is so explained genuinely has beliefs and desires which are causally responsible for the behavior in question. The question I pursue here is whether some animal behavior is properly so explained, that is, do (some) animals have beliefs and desires, and is (some of ) their behavior caused by these mental states? The assumption with which I begin – that belief-desire explanations are causal explanations which commit us to the claim that there are beliefs and desires – leads to three theses: that beliefs and desires are representations, that beliefs and desires are distinct, and that those organisms that have beliefs and desires are able to act in what I call a “goal-directed” manner. 2.1. Representationalism To attribute beliefs to an organism is to attribute to it internal states which vary with perceived qualities of the world. Beliefs are at minimum representations, internal states that aim to depict the way the world is. That is, in believing, one believes that the world is a particular way; one represents the world as being that way. If a dog believes that you are his master, then the dog has an internal state which represents you as being his master. For the purposes of this essay, I speak of desires as if they are representations as well – internal states that depict the world as the agent would have it be; however, as some who are committed to the reality of folk-psychological explanation are not committed to the claim that desires are representations, I remain agnostic as to their true nature. Thus, to ask whether animals have beliefs and desires is to ask if they represent their goals and means to achieve those goals. An animal that has beliefs and desires and that acts on those beliefs and desires represents both the way the world is and the way it would have the world be, and both of these representations contribute to the animal’s actions, to its bringing about the satisfaction of its desires. 

See, for example, Dretske ().

Attributing mental representations to animals



2.2. Distinctness If, whenever I saw a piece of cake, I ate it, no matter what my state of hunger (or the presence or absence of any other relevant desires) might be, then my desires would play no role in bringing about my eating of the cake. Similarly, if I always engage in eating behavior, whether there is a cake present or not, then my eating behavior on this occasion, in the presence of cake, is not properly explained by citing my belief that cake is present. Belief-desire explanation requires a causal contribution from both beliefs and desires. Moreover, implicit to the idea of beliefdesire explanation is that we can project from a particular explanation citing particular beliefs and desires to future behaviors also caused by those beliefs and desires. If the proper explanation for my cake-eating behavior involves my belief that cake is present, then, as long as nothing has changed, if I believe you want cake, then absent countervailing desires I will offer you cake, something I would not do if I did not believe cake was present. This ability to project from an explanation of one behavior to other behaviors requires that the beliefs and desires must be distinct, that individual beliefs and desires are able to enter causal transactions without each other. Of course, we might reject the idea that belief-desire explanations should be projectable. Causal realism about belief-desire explanation does not entail that the properties cited in such explanations are capable of entering into causal transactions other than those cited in a particular explanation. Nonetheless, the commitment to distinct representations should be maintained. Having both beliefs and desires is an advantage – even if a particular belief is not able to enter into causal transactions with any but a particular desire – because they are sensitive to distinct states of the world. Beliefs trigger actions based on what is the case, whereas desires trigger action based on what the agent would have be the case. These two triggers together are more valuable than either might be alone, as would be the case were they not represented distinctly. A bee that forages for pollen here and now because it has distinct representations of the presence of pollen here and the need for pollen now will be better off than a bee that forages based only on the presence of pollen or based only on the need for pollen. To ask whether animals have beliefs and desires is to ask if they have distinct representations of their goals and the means to achieve those goals.



The philosophy of animal minds 2.3. Goal-directed behavior

An organism whose behavior is caused by, and thus appropriately explained by, its beliefs and desires has distinct representations of its goals and means to achieve those goals. These distinct representations allow it to behave in a goal-directed, rather than merely goal-oriented, fashion, in the sense I describe below. That means that evidence that an organism’s behavior is goal-directed, rather than merely goal-oriented, is evidence that the organism’s behavior is caused by its distinct representations, that it has beliefs and desires. Goal-directed behavior is behavior directed toward achieving a goal, whereas goal-oriented behavior is behavior oriented, say by evolution, so that if it is performed in the right environment, it will lead to a goal. A representation of the goal does not play a role in causing goal-oriented behavior. For example, heliotropic flowers that turn their heads to follow the sun do so because chemicals (the growth hormone auxin) react to the sun’s rays (specifically to light of the shorter, blue wavelengths) by moving from the light and causing greater growth on the side of the plant away from the light and thus tilting the head of the plant toward the sun. The goal of following the sun is not represented by the plant, nor does the plant even represent the position of the sun in the sky. Instead, evolution has structured the plant in such a way as to guarantee that the plant will follow the sun across the sky. Such movement is goal-oriented: it is oriented by evolution to achieve a particular goal, but that goal plays no role in the present movement of the plant. Similarly, when the greylag goose engages in its stereotypical egg-retrieval behavior – it will use its bill to roll errant eggs back to its nest, but it will continue to move its bill as if rolling an egg even if the egg is no longer in place – the individual goals of the goose play no role in causing this behavior. Instead it performs this behavior as a result of the way evolution has structured it to behave, as a consequence of many generations of selection in which those geese who performed this behavior in a stereotypical fashion produced more viable offspring than their conspecifics. One might put this point in intentional terms (which are not intended literally!): evolution has the goal of producing as many viable greylag goose offspring as it can and so it orients the goose in such a  

See Saidel ().  See Galen (). See Galen () for a sample of the simple sorts of experiments that demonstrate this conclusion. The experiments she cites include blocking light of particular wavelengths, removing the heads of plants, and blocking parts of the flower stem from the sun by applying liquid paper!

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way as to ensure that the goose engages in behavior that has the result that this goal is met. Goal-directed behavior, on the other hand, is behavior which is caused in part by (a representation of ) the goal. When an organism’s behavior is goal-directed, then the organism does what it is doing in order to achieve a goal it has. When a rat navigates a maze to reach the food at the end, it seems to be engaged in goal-directed behavior. When a chimpanzee places a nut just so on a stone and pounds it with a stick, opening the nut and releasing the meat inside, it seems to be engaged in goal-directed behavior. When an organism behaves in a goal-directed fashion it is able to overcome obstacles that might otherwise prevent it from reaching its goal. When the organism’s behavior is goal-oriented, it can only overcome those obstacles that evolution has foreseen and accounted for. The question this essay asks – does animal behavior ever provide evidence that it is caused by the animal’s beliefs and desires? – can be answered by answering the question suggested here: does animal behavior provide evidence that it is goal-directed rather than goal-oriented? Even an organism which represents its environment would be unable to engage in goal-directed behavior if its representations of its goals and the means to achieve those goals were not distinct. If evolution had so structured the organism that it lacked distinct representations of its goals and the means to achieve those goals, then the organism would not be able to abandon a particular behavior – a means to a goal – while retaining the goal. Having distinct representations allows an organism to abandon one behavior and adopt another while still retaining the goal that the previous behavior was aimed at achieving, and toward which the new behavior is now directed. Having distinct representations of goals and means to achieve them is thus a prerequisite for behaving in a goal-directed fashion.

2.4. Learning This means that those animals which engage in goal-directed behavior, those animals which represent their goals and the means to achieve those goals distinctly, are capable of a type of learning that other animals are incapable of. Specifically, they have the ability to form new associations of goals and the means to achieve those goals. Not all learning is evidence of distinctly represented goals and means to achieve those goals. Some learning does not implicate representation,

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The philosophy of animal minds

of means or of goals, at all. For example, swamp sparrows learn their species-specific song as a consequence of hearing the song (or a variation of it) at a particular moment in their development. They are merely filling in gaps in an evolutionarily pre-programmed behavioral pattern. This learning is functionally similar to that of the goose which imprints on whatever large moving object happens to be at hand when it is an infant. No representation of the goal of learning the song is implicated. Nor does their learning exhibit the ability to overcome obstacles that is characteristic of goal-directed behavior. Similarly, conditioned learning fails to implicate representations. When the pigeon learns to peck at a particular spot or at a particular time in order to achieve food, it is merely altering where or when it pecks for food. It is not learning a new means to get food. This inability to adopt a new means to achieve an (old) end is nicely illustrated in a pair of articles by Keller and Marian Breland (, ). In their first article they practically crow in anticipation of all the various tasks they can use conditioning to train animals to perform. But within a decade they found that their training methodology was limited. If the reward was food, then they could only train their animals – including such “intelligent” creatures as pigs – to behave in ways that they were equipped by evolution to behave in order to receive that reward. Thus, for example, pigs could learn to associate rooting behavior with food, but for the goal of food, they could not learn to perform a behavior that did not involve rooting. In the plainest example of this, the Brelands attempted to condition pigs to not root in order to receive food. The pigs would starve instead. They would become hungry and as a consequence of their hunger they would root, and so they would not receive food. The learning that counts as evidence of the presence of representations is learning that cannot be explained by conditioning or by a filling in of behaviors in evolutionarily pre-established patterns. These sorts of learning are stereotypic responses to the organism’s environment. The organism’s adoption of novel behavior is narrowly structured by its environment to take advantage of certain regularities in that environment (e.g., the songbird will be in an environment which includes other songbirds of the same species, the pigeon will be in an environment in which pecking will prove useful in obtaining food and in which certain sources of food will regularly yield food). We will have evidence of mental representations in animals when we have evidence of animals which are able to respond to 

See, for example, Marler ().

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their environment plastically, for example by adopting a wholly novel way of getting food. I started by asking what would count as evidence that (non-human) animals have, as causes of their behavior, beliefs and desires. I have argued that we’re looking for evidence of distinct representation of an animal’s goals and the means to achieve those goals, and that one source of evidence for this is in an animal’s ability to learn to adopt new means to achieve a goal, or to adapt old behaviors as means to achieve new goals. This sort of evidence can be top-down – that is, evidence that comes as the result of theoretical considerations – or bottom-up: that is, evidence that comes as a result of examining changes in animal behavior. In section  I give a top-down argument, and in section  I discuss evidence that provides bottom-up support for the thesis that some animals do learn in the requisite manner and are thus likely to have distinct representations of their goals and means to achieve those goals, that they have desires and beliefs which help them satisfy these desires. 3. the evolution of distinct representations of means and ends I make two assumptions for the purposes of the argument presented in this section. First, I assume that human beings are able to act on distinct representations of their goals and means to achieve those goals. One might think of the conclusion I reach as being conditional: if human beings have distinct representations, then considerations of evolution give us good reason to think that some animals do too. Second, I assume that this trait – the ability to represent one’s goals and means to achieve those goals distinctly – is present in the human phenotype because of the advantages in fitness that have accrued to those of our ancestors who had this trait in virtue of their having it. That is, the trait is present because of the advantages that it caused, rather than advantages with which it was concomitant. The fact of the matter is, of course, beyond the reach of discovery; nonetheless, this seems a plausible assumption. There are three basic evolutionary goals each organism has: to feed, to avoid predators, and to reproduce. When the members of a species are able to satisfy these goals, the species will flourish; when they fail to satisfy 

That is, I assume that the ability to represent one’s goals and the means to achieve those goals distinctly was itself “selected for” and is not present as a consequence of “selection of ” some other trait, in the language introduced by Sober ().

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The philosophy of animal minds

these goals, the species will die out. Merely being able to satisfy these goals is good, but being able to overcome obstacles to satisfying these goals is even better. One way that evolution can equip organisms to overcome obstacles to their evolutionary goals is by recognizing those obstacles ahead of time and programming the organisms with strategies for overcoming those obstacles. An example of behavior that is able to overcome the individual’s obstacles, but which may be accounted for by evolutionary programming rather than as a consequence of representing one’s goals and recruiting new means to achieve those goals, is the piping plover’s various strategies used to keep predators away from its eggs. These include camouflaging its nest, peeping loudly while walking near an intruder (apparently in order to distract the intruder from the nest), flying slowly and conspicuously near the intruder (also apparently in order to distract the intruder from the nest), brooding on a false nest (where there are no eggs), and leading the intruder away from the nest by walking as if its wing were broken. Whatever the true explanation of the plover’s behavior may be – perhaps its behavior is truly caused by distinct representations the plover has of its goals and different means it might use to achieve those goals – one way that evolution may have equipped the plover to respond to intruders was to give it various non-plastic responses to possible predators, each triggered by, say, the plover’s sense of the distance the intruder is from the nest. Then we can imagine that evolution has equipped the plover with the following conditionals: “If the intruder is distant from the nest, brood a false nest 

 

I use intentional idioms hesitantly. Evolution doesn’t plan anything; it is not an intelligent agent. When I use an intentional idiom it is merely shorthand for a much longer account of what might actually have happened. For instance, in the example discussed in the text, the more accurate explanation for the plover’s behavior doesn’t involve any pre-cognition of the possible threats to its eggs, but instead involves selection for the ancestors of the current generation of plovers as a consequence of their having more offspring than their conspecifics because they (the ancestors) were likely to, for example, brood on a false nest in response to a nearby predator. The greater relative survival of the eggs of the false-brooder as compared with its conspecifics which did not false-brood, coupled with the heritability of this behavior, ensured the presence of false-brooding in future generations. See Ristau (). I wish to be careful here: I do not mean to suggest that I am giving the correct explanation for the plover’s behavior, or even that I am in the business of giving that explanation. I am merely using the variety of responses available to the plover – responses that seem to be both various and limited – as an example of how evolution might equip an organism to surmount obstacles in the path of achieving its evolutionary goals. If plovers are programmed by evolution to have these responses, then I imagine that the triggers for their behavior are more complex than the distance the intruder is from the nest. The triggers most likely also include the size of the intruder, whether the intruder is able to fly, the age of the eggs, the orientation of the intruder, and the speed of the intruder, to name just a few possible cues to which the plover might be sensitive.

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away from the actual nest”; “If the intruder is closer to the nest, fly slowly near the intruder to distract it from the nest”; “If the intruder is even closer to the nest, lead the intruder away from the nest with a broken wing display.” Such behavior is only successful when the obstacles the organism typically faces are included in its behavioral programming. If a predator navigates by the smell of the eggs (suppose that the calcium in plover eggs gives off a particular aroma to which this predator is sensitive) and not by visual cues such as spotting the nest or an easy prey, then the predator will not be distracted by any of the plover’s various displays and it will eat the eggs. In such a circumstance, the successful animal will be the one that can respond to the challenge presented by this predator. It will have to recognize that the predator is not distracted by the displays it customarily uses to distract predators, and then it will have to recruit a new behavior to defend its nest. That is, the successful animal will have to generate a new representation of its environment, jettison the old representations that are linked to its goal of protecting its nest, and link that old goal to a new behavior aimed at achieving that goal. Perhaps there are no such predators in the plovers’ environment, or perhaps such predators are rare enough that it is not worth the evolutionary cost to develop resources to deal with them. Or perhaps the plovers haven’t had the right mutations which might lead to selection for the resources needed to respond to their environment in quite so plastic a fashion. Whatever the case may be for the plovers, there are animals whose evolutionary environment was so variable that those who were able to represent their goals and their means to achieve those goals distinctly were better fit than those who relied on evolutionarily selected strategies for success: human beings. Did this trait evolve after the hominid and primate lines split or before? It will come as no surprise that my answer to this question is that the trait evolved before the split. The evidence needed to demonstrate the presence of distinct representations of goals and means to achieve those goals is variation in behavior that cannot be explained by the interaction of a few simple conditionals. Chimpanzee feeding behavior is suggestive in this regard. The chimpanzee population occupies several different niches, each with different sources of food. For example, some chimpanzees live in the relatively austere region of Sierra Leone in West Africa in which they have found the fatty fruit of the Kapok tree to be an important resource. Some of these chimpanzees have found that using sticks to cover their feet makes accessing the fruit on the thorny trees to be a less painful

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The philosophy of animal minds

experience. Chimpanzees in other regions eat nuts, which requires that they learn how to crack open the nuts in order to reach the meat. Some chimpanzees “fish” for termites by stripping leaves from sticks of a certain size, inserting the sticks into the termite mounds and then carefully maneuvering the sticks to withdraw them with the termites still attached (care is needed so that the termites aren’t brushed off the stick in the process of removing it from the mound). As Matsuzawa and Yamakoshi () report, chimpanzee feeding behavior is both varied and determined by their environment. They describe two communities, the Bossou and the Nimba, which live approximately  kilometers from each other, separated by – kilometers of savannah. The Nimba crack and eat the fruit of the Carapa Procera tree, the Bossou eat the leaves, flowers, and gum on the trunk, but not the nut in the fruit; Bossou use twigs to dip for Safari ants (they strip the twig of leaves), but the Nimba do not dip for Safari ants although they are available (and neither group fishes for termites); the Bossou use leaves for drinking water, but the Nimba do not. This is just a sample of the variations found in chimpanzee feeding behavior between different groups. This variation is too great – especially between populations that are so close to each other that genetic intermingling seems likely – for it to be explained by a series of evolutionarily (and thus genetically) determined conditionals; instead, their feeding behavior is more closely structured by the food that is available in their current environment and the trial and error that comes with associating new representations with old goals, with adopting new means to achieve those old goals. Of course, while this variation in chimpanzee habitat and the consequent variation in chimpanzee feeding behavior suggests that the chimpanzee feeding behavior is a result of their representations of their goals and means to achieve those goals, it is only suggestive of this conclusion. Perhaps there is some other way that evolution has equipped these animals to achieve their goal of attaining food in diverse environments. Suppose this is the case. Then we are left with a puzzle: both humans and chimpanzees are able to adapt their behavior to meet their goals in various environments. According to this supposition, chimpanzees do so without distinct representations of their goals and means to achieve their goals. Humans, on the other hand, do distinctly represent their goals and means to achieve their goals. What was the engine driving the selection of this trait in human beings? It could not have been the advantage in fitness an organism gains by being able 

See Alp (). I discuss this behavior further in section .

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to respond appropriately in various environments; the ancestral condition already provided this advantage. Absent any satisfactory solution to this puzzle, that both humans and chimpanzees have the ability to achieve their goals despite variations in their environments suggests the same traits support this ability in both species, namely that chimpanzees and human beings both represent distinctly their goals and their means to achieve those goals. It should be noted that a possible solution to this puzzle is selection for language use. According to this proposal, primitive language use somehow led to primitive use of representations, which then better enabled language use, and so, through selection for minds better able to use language, there was also the development of distinct representations of goals and means to achieve those goals. If this is right, then chimpanzees – a species in which there was no selection for language use – are able to adapt their behavior to meet their goals in different environments without distinct representations of their goals and means to achieve those goals, whereas human beings do have distinct representations as a consequence of selection for language use. Discussing this proposal fully would take me too far afield here, but it is worth noting that it merely shifts the argument from one about evidence for the presence in animals of distinct representations of goals and means to achieve those goals to one about evidence for primitive language use in animals. For key to this proposal is that there was no selection for primitive language use in animals. If we think that primitive language use led to the ability to represent one’s goals and means to achieve those goals distinctly, and we also think that some animals have primitive language use, then this proposal merely describes how distinct representations might have evolved, but it doesn’t place that evolution before the hominid/primate split. What we need is evidence that some animals have (or lack) the ability to represent their goals and means to achieve those goals distinctly. 4. goal-directed behavior in animals In section ., I argued that evidence that an animal’s behavior is caused by its beliefs and desires is provided by the animal’s ability to recruit new  

I am indebted to Tad Zawidzki for raising this objection. See Allen and Saidel () for an argument that primitive elements of language can be found in animals. It is worth noting as well that this proposed solution to the puzzle violates the assumption I made at the beginning of this section, that the trait of having distinct representations of one’s goals and means to achieve those goals was itself directly selected for. Of course, if the solution works, then the assumption is incorrect.

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The philosophy of animal minds

means to achieve old goals. This learning is evidence that the animal has separate representations of its goals and the means by which it might achieve those goals. In this section I present five examples of animals adopting new behaviors, one involving monkeys, the others involving chimpanzees and bonobos. All but the last of these examples feature wild animals observed in their natural habitat (although the first involves some interaction with humans). I argue that these are demonstrations of the kind of learning that is relevant here, thus that at least this behavior was caused by these animals’ beliefs and desires. () A notorious example is that of the sweet-potato-washing macaques of Koshima Island, Japan. These macaques were provided with sweet potatoes (for food) by their caretakers. The potatoes were deposited in the sand, which resulted in their being covered with sand. Most of the macaques would brush the sand off the sweet potatoes before eating. However, in , Imo, an -month-old female, began bringing her food to a stream to wash it before eating it. Soon (about a month later) one of Imo’s playmates (fellow juveniles) started washing sweet potatoes, followed (within two more months) by Imo’s mother and another playmate. Eventually – within a decade – sweet-potato-washing became common in the troop. Sweet-potato-washing was not the only novel behavior Imo introduced to her troop. The macaques were also provided with wheat, which also became sandy and thus, we can assume, unpleasant to eat. Imo learned to carry the wheat to the water and drop it in. The heavier sand fell to the bottom while the lighter wheat floated on the surface. She then retrieved and ate the wheat. This placer-mining behavior also spread through the troop, albeit more slowly than did the potato washing. () Rosalind Alp () reports observations of chimpanzees in Tenkere, Sierra Leone, West Africa using sticks as quasi-shoes. The range these chimpanzees occupy is poor in food resources, which means that the fat-rich fruit of the Ceiba pentanda (Kapok) tree is a valuable source of nutrients. However, feeding on the fruit has to be very painful, as   

There are many suggestive examples I do not discuss here, including, in addition to primates, animals in other families. There has been considerable debate about the best explanation of the sweet-potato-washing behavior exhibited by the Koshima macaques. For a small sample, see the discussion below. Was the behavior truly novel? Does Imo truly deserve credit for introducing this behavior? Was Imo a cognitively gifted individual who single-handedly changed the behavioral patterns (and perhaps the evolution) of her troop? I discuss these and related issues below. I argue that the answer to these questions doesn’t matter for my purposes.

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the branches of the tree are covered with thorns. Alp reports observing several individuals removing bare branches, gripping them between their greater and lesser toes, and using the branches to protect their feet as they perched on the tree, feeding on the fruit. These individuals would continue to hold the branch with their toes as they stood and reached for more fruit, even as the foot was no longer resting on the tree, positioning the branch under the foot again as they returned to squatting and eating. Alp also observed individuals placing a similar branch for use as a seating platform. Unlike the washing behavior of the macaques, the use of a branch as a protective tool did not spread through the population (at least not as of the time of Alp’s report), nor was it common for the individuals who did use it: for example, one individual used a “stepping-stick” for only about ten minutes of several hours during which Alp observed him feeding in the Kapok tree. () As noted in Section , evidence of chimpanzee nut-cracking behavior is widespread. Christophe Boesch () observed chimpanzees learning to crack nuts. Nut-cracking involves placing a nut on an appropriate (usually stone) surface – the anvil – and then hitting it with another stone or a branch – the hammer. Boesch notes that although chimpanzees generally carry their hammers with them, mothers with -yearold infants will often leave their hammers behind, by their anvils, while they forage and their infant also remains near the anvil. This carries some risk: other chimpanzees may steal the hammer, or the infant may use and damage or refuse to return the hammer, forcing the mother to find a new hammer. Boesch also observed mothers placing nuts on the anvil for their young, as well as a mother stopping her child from trying to open a nut haphazardly placed on the anvil until she had cleared the anvil off and replaced the nut carefully. Boesch’s most striking observation was of an apparent instance of teaching: On  February , Ricci’s daughter,  year old Nina, tried to open nuts with the only available hammer, which was of an irregular shape. As she struggled unsuccessfully with this tool, alternately changing her posture, hammer grip and the position of the nut, Ricci was resting. Eventually, after  min of this struggle, Ricci joined her and Nina immediately gave her the hammer. Then, with Nina sitting in front of her, Ricci, in a very deliberate manner, slowly rotated the hammer into the best position with which to pound the nut effectively. As if to emphasize the meaning of this movement, it took her a full minute to perform this simple rotation. With Nina watching her, she

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The philosophy of animal minds then proceeded to use the hammer to crack  nuts (of which Nina received six entire kernels and a portion of the other four). Then Ricci left and Nina resumed cracking. Now, by adopting the same hammer grip as her mother, she succeeded in opening four nuts in  min. Although she had difficulties and regularly changed her posture ( times), she always maintained the hammer in the same position as did her mother. (Boesch [], p. )

() Tetsuro Matsuzawa () reports two cases of tool construction, one of which involves using a tool to create a tool. In the first, chimpanzees broke either their hammer or their anvil during nut-cracking and then chose one of the pieces to use as a new hammer. One might think that this is merely a case of replacing a broken hammer with an available stone tool, but on some of these occasions, the original hammer did not break: the anvil broke and the chimpanzee recognized one of the resulting pieces as a better hammer than the one s/he had been using. Matsuzawa also observed three separate chimpanzees using a stone wedge to prop up the anvil, thus making the surface of the anvil more level and the anvil more stable. () Finally, Susan Savage-Rumbaugh, Nicholas Toth, and Kathy Schick () report that they were able to teach Kanzi, a domesticated bonobo who has learned some sign language, how to knap stone. Knapping stone is the process whereby our Stone Age ancestors (Homo sapiens) would strike one stone on another in order to create arrowheads and spear points. Toth demonstrated knapping for Kanzi, and they motivated Kanzi by providing him with a box with grapes inside. Kanzi had to knap stone to manufacture a tool which he could then use to open the box to fetch his reward. He was able to do so. In each of these examples the animals take an old goal and adopt a new means to achieve that goal. Several aspects of the newly adopted behaviors are worth noting. First, even though the goals all involved acquiring food, of these behaviors – washing food, placing a stick under one’s feet, cracking nuts this way rather than that way, replacing a hammer, propping an anvil, and knapping stone – only cracking nuts is part of the typical behavioral repertoire associated with feeding. Remember the puzzle Breland and Breland () encountered: when the reward was food, they were unable to condition their subjects to perform behaviors not typically associated with acquiring food in order to receive the reward. Even if, as some commentators have observed, some of these novel behaviors are not completely novel to these animals, they are now performing these behaviors in order to achieve goals toward which they were not originally directed. That means

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that even if the means to achieve the goal is not new, the animals are creating new means–ends pairings. As I remark above, the exploits of the Koshima Island macaques are notorious. Many commentators are incredulous, doubting that this behavior is genuine as described. For just one example, consider Galef (), who suggests that the behavior of the Koshima macaques might be the result of rewards from their caretakers, that those who wash their sweet potatoes might be fed first. (But see de Waal [] for an opposing view.) Even if this is so now, it wasn’t the case then: Imo did not learn to wash her food as a consequence of conditioning. Conditioning works by reinforcing the association of a behavior with a stimulus. The association of the behavior and stimulus cannot be reinforced if it does not already exist. Thus conditioning cannot explain the novel behavior that Imo initiated; at best it might explain Imo’s continued potato-washing. This response to Galef ’s challenge illustrates my general response to challenges of this sort. I think these challenges miss the true significance of the newly adopted behaviors in protesting that these behaviors are not wildly significant. Imo may not have been a macaque Leonardo, inventing strategies generations ahead of her peers; it is significant enough that she initiated a new behavior, that she learned a new association of a behavior and a goal. Mithen (), for example, worries about the chimpanzee creation of tools, such as sticks used to fish for termites, that their tools are not as complex as those humans create, that the movements used to create them are the same as movements used to perform other behaviors, that each tool is used for just one task rather than being adaptable for use in achieving other goals, and that chimpanzee tool-learning behavior is laborious, rather than intuitive. Of course, he is correct about each of these observations, but despite this, in creating and in learning how to create tools, those chimpanzees are forming new representations of ends and means to achieve those ends. This is only possible if they have beliefs about their environment and desires toward the achievement of which their behavior is directed. One might object that these examples of learning are simply instances of “monkey see, monkey do” imitation. Perhaps primates are wired by evolution to copy behaviors of those around them, and so instead of adopting a new behavior as a means to achieve a goal, implicating the separate representation of the behavior and the goal, these are examples 

Mithen’s conclusion is that chimpanzees lack what he calls “technical intelligence,” which human beings have. Perhaps he is right about this, but this is beside the point here.

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The philosophy of animal minds

of primates merely mindlessly copying the behavior they observe. Several details of these examples make this interpretation unlikely. () The other members of Imo’s troop did not imitate her immediately. In fact, it took a month for even one other macaque to wash sweet potatoes, several months for others to join in, and years for the entire troop to adopt the behavior. Moreover, when Imo initiated another novel behavior, the troop took even longer to adopt the behavior. If this were simply an example of behavior being mimicked, we would expect other members of the troop to copy the behavior, even if they did not adopt the behavior, much earlier. () The first Tenkere chimpanzee to use a stepping-stick was not copying any behavior in doing so, and if other members of the group copied him, they did not do so in any obvious fashion. He used the stick in front of others, who continued to feed with their feet unprotected. () Nina did copy her mother’s grip on the hammer, but, as Boesch notes, she did not copy other aspects of her mother’s behavior; she had difficulties cracking the nuts even after her mother showed her the proper grip, difficulties which led to her changing her posture several times as she cracked nuts. Imitation this is, but it is goal-directed imitation, not rote copying. () Propping the anvil is, like creating a stepping-stick and washing sweet potatoes, a novel behavior that could not have arisen as a consequence of copying. Each of these behaviors is also unlikely to come about as result of chance. I think the conclusion here is clear. These are examples in which the animals are able to retain a goal while adopting a new method of achieving that goal. This means that they represented the goal and the means to achieve that goal distinctly. I think that we can truly say of these animals that they wanted to achieve their goals and that they had beliefs about how to do so. 5. concluding remarks I have focused my attention on the goals toward which behavior is directed without saying much about representations of the means to achieve these goals. One might naturally wonder if the learning that I am highlighting might not be possible without representations of one’s means to achieve one’s goals at all. If an animal has a representation of a goal which is not tied to a particular behavior, could it not maintain that goal while adopting new behaviors aimed at achieving the goal without representing

Attributing mental representations to animals



the behaviors? This seems right: much of what we do, we apparently do without representing our activity. For example, we reach with our hands just so to grab a glass, and if the glass is too large or too small, we alter our grip without thinking about it. Perhaps the learning I describe above is similarly an unrepresented adjustment. In fact, the learning I have described could not be this sort of unrepresented adjustment. Hammering open nuts is not a slight adjustment from termite-fishing (or whatever means of foraging the ancestors of nutcracking chimpanzees used). Nor could it be a conditioned response to a novel environment. As discussed above, conditioning reinforces preexisting behaviors, but we don’t see evidence of nut-cracking behavior in those chimpanzees that don’t crack nuts: the behavior is not there to be reinforced. Moreover, were conditioning the explanation for the kind of learning cited in section , then we would expect more random behaviors prior to conditioning, and there is no evidence of that. The Tenkere chimpanzees who fashioned “stepping-sticks” were not randomly behaving only to stop when their behavior was reinforced (by pain-free eating). If anything, their behavior was exactly the opposite of this: they ate (with pain), they stopped eating, fashioned a “stepping-stick,” and then commenced eating again. Then they abandoned the “stepping-stick” despite the reinforcement that supported its use. These examples of learning are illustrations of problem-solving behavior involving representation of solutions, of means to achieve goals, rather than mindless actions that happen to hit upon means to achieve goals. The story doesn’t end here. Human mental representations are often described as “propositional attitudes.” They can be manipulated in ways that resemble a language. Can the same be said for animal mental representations? Humans don’t merely believe and desire; we also hope, fear, anticipate, and engage in a variety of mental attitudes so complex that they can only be described by complex German words (e.g., “schadenfreude”). Is the same true for animals? Human mental states exhibit opacity. Do animals’? These are questions for further study. For now, I have argued that we see in some animal behavior evidence that the animals are behaving in a goal-directed fashion, and that therefore they are making use of representations of means to achieve their ends and representations of their ends in order to satisfy their goals. This means that we are right to attribute to these animals beliefs and desires in order to explain this behavior.

chapter 3

Chrysippus’ dog as a case study in non-linguistic cognition Michael Rescorla

1. introduction: language and thought Do non-linguistic creatures think? Debate over this question tends to calcify into two extreme doctrines. The first, espoused by Descartes, regards language as necessary for cognition. Modern proponents include Brandom (, pp. –), Davidson (a, pp. –), McDowell (), and Sellars (, pp. –). Cartesians may grant that ascribing cognitive activity to non-linguistic creatures is instrumentally useful, but they regard such ascriptions as strictly speaking false. The second extreme doctrine, espoused by Gassendi, Hume, and Locke, maintains that linguistic and non-linguistic cognition are fundamentally the same. Modern proponents include Fodor (), Peacocke (), Stalnaker (), and many others. Proponents may grant that non-linguistic creatures entertain a narrower range of thoughts than us, but they deny any principled difference in kind. An intermediate position holds that non-linguistic creatures display cognitive activity of a fundamentally different kind than human thought. Hobbes and Leibniz favored this intermediate position. Modern advocates include Berm´udez (a), Carruthers (, ), Dummett (, pp. –), Malcolm (), and Putnam (, pp. –). Proponents may grant that our lower-level cognition resembles the mental activity of languageless creatures, but they insist that we also manifest higher-level cognition unavailable to such creatures. The main challenge facing such a view is to describe non-linguistic cognitive processes that differ in a principled way from higher-level human thought. I will try to meet this challenge by exploring a putative example of non-linguistic cognition. Tolman () introduced the notion of cognitive



I am indebted to Jos´e Luis Berm´udez, Robert Lurz, and Jason Newman for helpful comments. M. Wilson () offers an historical overview.



Chrysippus’ dog as a case study



maps to explain how rats in a laboratory maze take detours and shortcuts to reach destinations. Although Tolman’s analysis proved controversial, many psychologists have followed him in proposing that human and animal navigation exploits cognitive maps. These have representational content: they represent the world as being a certain way, so we can evaluate them as veridical or non-veridical. Moreover, they are involved in rational mental processes that update them based on perception and exploit them during locomotion. Thus, cognitive maps are genuinely cognitive. Yet they differ from higher-level human thought in two crucial ways: they do not have logical form, and they do not figure in deductive inference. To illustrate the explanatory potential of cognitive maps, I will deploy them against a venerable philosophical argument for languageless thought and reasoning. Sextus Empiricus, who credits the argument to Chrysippus, presents it as follows: [Chrysippus] declares that the dog makes use of the fifth complex indemonstrable syllogism when, on arriving at a spot where three ways meet . . ., after smelling at the two roads by which the quarry did not pass, he rushes off at once by the third without stopping to smell. For, says the old writer, the dog implicitly reasons thus: “The animal went either by this road, or by that, or by the other: but it did not go by this or that, therefore he went the other way.”

Many commentators, including Aquinas, Gassendi, Jevons, Montaigne, and even King James I of England, have argued on this basis that nonlinguistic creatures execute logical reasoning. More recently, Glock () and Horgan and Tienson (, p. ) concur. Despite millennia of discussion, opponents of non-linguistic deduction have not yet answered this argument satisfactorily. They have not shown how to explain the described phenomena without imputing logical reasoning to Chrysippus’ dog. I seek to fill this gap. My proposed explanation, which draws heavily upon research from contemporary robotics, cites rational, non-deductive mental processes defined over cognitive maps. The explanation shows that we can accommodate Chrysippus’ dog without assimilating animal minds to human minds. Yet it also militates against Cartesianism, because it invokes representational mental states and rational processes defined over those states. Thus, my treatment illustrates the explanatory resources of an intermediate position that countenances non-linguistic cognition while sharply distinguishing it from linguistic cognition. 

From Outlines of Pyrrhonism, I., as translated in Floridi ().



The philosophy of animal minds 2. logical form and cognitive maps

I focus on two crucial features of human propositional attitudes: they have logical form, and they participate in deductive reasoning sensitive to that form. Both features have been recognized since Aristotle, but Frege profoundly enhanced our understanding of them. The Fregean tradition, culminating in Tarski’s work, analyzes how truth-conditions of logically complex thoughts depend on semantic values of their parts. It thereby illuminates the semantically and inferentially relevant structure of human thought (Burge [], pp. –). The paradigmatic structural elements are compositional mechanisms of the predicate calculus: truth-functional connectives, universal and existential quantifiers, and predication. Additional compositional devices, such as modal operators and generalized quantifiers, have also received compelling semantic analysis over the past century. My question is whether non-linguistic animals enter into mental states with logical form, where “logical form” minimally includes the familiar compositional mechanisms of the predicate calculus. As already indicated, I will study a single putative case of non-logical cognition: spatial representation. The phrase “cognitive map” appears frequently in contemporary psychology and philosophy. As Berm´udez (, pp. –) and Kitchin () document, researchers associate it with diverse meanings. Gallistel (, p. ) defines a cognitive map as “a record in the central nervous system of macroscopic geometric relations among surfaces in the environment used to plan movements through the environment.” This definition remains neutral about the extent to which cognitive maps resemble ordinary concrete maps. I will employ a more literal usage: a cognitive map is a mental representation that represents geometric features of the physical environment and that employs the same basic representational mechanisms as a concrete map. On this usage, which seems close to that of O’Keefe and Nadel (, pp. –, –), cognitive and concrete maps share a common representational format. Specifically, they have comparable compositional structures. A fuller elucidation of my usage would require systematic discussion of the compositional mechanisms underlying concrete cartographic representation. Unfortunately, those mechanisms are not completely understood. Despite the efforts of such authors as Casati and Varzi (), Pratt (), and A. Sloman (), we have no canonical cartographic semantics analogous to Tarski’s semantics for the predicate calculus. Nevertheless, my definition of “cognitive map” seems clear enough for present purposes.

Chrysippus’ dog as a case study



I will assume that cognitive maps do not have logical form. This assumption follows from two premises: first, ordinary concrete maps do not have logical form; second, cognitive maps and concrete maps employ the same basic representational mechanisms. Regarding the first premise, most philosophers who address the matter agree that maps do not express negation, disjunction, the conditional, or the quantifiers (Fodor [], p. ; Millikan [], p. ; Pylyshyn [], pp. –). This verdict merits more extended defense than it typically receives, but it seems plausible enough for us to assume it here. Whether concrete maps can express conjunction strikes me as more debatable, but I will again assume a negative verdict. More debatable still is the thesis that concrete maps do not feature predication. One might propose that attaching a map symbol (such as a symbol denoting mountains) to a map coordinate amounts to predicating the corresponding property of the corresponding spatial location. Casati and Varzi () develop a formal cartographic semantics designed to incorporate this proposal. If the proposal is correct, then maps feature rudimentary logical form akin to atomic sentences. I attack this proposal in Rescorla (in press b), arguing that maps do not feature predication, as construed within Fregean or Tarskian semantics. However, suppose we were to concede that maps have predicative structure. A system of cartographic mental representations would still not support deductive reasoning. It would not allow familiar inference patterns like modus ponens, argument by cases, or universal instantiation. Not even the laws of identity would apply, since concrete maps do not feature an identity sign. Thus, even if we were to concede that cognitive maps have predicative structure, a principled distinction would separate mental processes defined over them from higher-level human cognition. Given that cognitive maps do not participate in logical inference, can they figure in any rational mental processes? Philosophers often suggest that they cannot, on the grounds that rationality requires logically structured mental states (Devitt [], pp. –; Pylyshyn [], pp. –; Rey [], p. ). If so, then mental activity defined solely over cartographic mental representations is not rational. Accordingly, many philosophers will resist calling the activity “cognitive.” To address these worries, I want to discuss an instructive case study: Chrysippus’ dog. I will analyze this case study through a detailed description of rational mental activity defined over cognitive maps. 

Camp () considers how one might enrich maps with semantic devices akin to negation, disjunction, the conditional, and the quantifiers. However, she seems to grant that these semantic devices go beyond ordinary cartographic representational mechanisms.



The philosophy of animal minds 3. explaining the phenomena

Discussions of Chrysippus’ dog typically choose among four strategies: () Treat the dog as executing a deductive inference. This strategy is probably the most popular. Fry (), who works within a cybernetic framework, develops it in fairly rigorous detail. Since logical reasoning presupposes logical structure, () requires us to ascribe logically structured mental states to the dog. () Attribute logical reasoning to the dog, but construe the attribution instrumentally. According to (), the dog does not “really” execute deductive inference. When we impute logical reasoning to the dog, we are interpreting its behavior, not describing observer-independent states and processes. Ironically, Sorabji (, p. ) suggests that Chrysippus himself advocated (). Similarly, Dennett (, p. ) suggests that our attribution of logical reasoning to Chrysippus’ dog is just a matter of adopting the “intentional stance.” () Do not attribute logical reasoning to the dog. Instead, maintain that the dog records relevant sensory observations regarding the third path. Plutarch favored (): “it is perception itself, by means of track and spoor, which indicates the way the creature fled; [the dog] does not bother with disjunctive and copulative propositions” (, a–b). Samuel Coleridge adopted a similar analysis. More recently, G¨ardenfors maintains that the dog “could have smelled the scent so clearly along the third path that it did not need to do any sniffing” (, p. ). () Grant that the dog records no additional relevant observations beyond those mentioned by Chrysippus. Explain the dog’s behavior by citing observer-independent mental processes distinct from logical reasoning. Proponents of () include Philo of Alexandria, Basil of Caesarea, and Ambrose. For instance, Ambrose () cites not logical inference but rather “the training given by nature” (vi..). Strategy () is inadequate. It denies what Chrysippus takes as a datum: that the dog forms no relevant observations of the third path. Setting aside whether Chrysippus himself saw a dog behave as he describes, many philosophers seem convinced that non-linguistic animals routinely exhibit similar behavior. To satisfy these philosophers, we must show that the behavior could result from a mental process other than logical reasoning. 

Floridi () offers a useful overview of the extensive historical literature.

Chrysippus’ dog as a case study



Strategy () is appealing only if we accept an instrumentalist or “interpretivist” approach to intentionality, like that espoused by Brandom, Davidson, or Dennett. I reject any such approach. From the realist perspective that I favor, creatures enter into representational mental states that depict the world as being a certain way, states whose representational contents do not result from interpretation by an observer. Cognitive psychology should isolate laws governing how representational mental states interact with one another, with perceptual inputs, and with behavioral outputs. As Fodor (, pp. –) argues, there is no more reason to adopt an instrumentalist stance towards the theoretical posits of cognitive psychology than towards those of any other science. Thus, we should reject an instrumentalist treatment of Chrysippus’ dog. Strategy () is more promising than () and (). The problem is that no one has developed it satisfactorily. Vague appeals to “nature” do not suffice. We must describe a psychological mechanism that differs in a principled way from logical reasoning, and we must show that the proposed mechanism generates the desired behavior. In my view, an adequate development of strategy () should satisfy three constraints. First, it should predict the desired behavior, rather than dismissing Chrysippus’ description in the style of strategy (). Second, it should support appropriate counterfactuals about how the dog would have behaved had circumstances been different. For instance, it should support the counterfactual: if the prey had traversed the second path, then the dog would have chosen that path rather than the third. Similarly, it should support the counterfactual: if the dog had sniffed the second and third paths without detecting the prey, then it would have chosen the first path without bothering to sniff. Finally, a good account should isolate a general cognitive mechanism that produces the dog’s behavior and that is deployable in diverse circumstances. To take an absurd example, we should not posit an innate “hunting at a three-way fork in the road” cognitive module with the following property: if the dog pursues its prey to a threeway fork in the road, and if the dog detects no signs that the prey traversed two of the three forks, then the dog immediately proceeds down the third 

Berm´udez (a, pp. –) denies that non-linguistic creatures execute logical reasoning, but he ascribes to them a more primitive mode of reasoning (“proto-logic”) not involving standard logical connectives. Specifically, he isolates a proto-logic analog to the inference “p or q; not-p; therefore q.” He does not apply his discussion of proto-logic to the case of Chrysippus’ dog. However, one might try to elaborate () by citing Berm´udez’s proto-logical analog to the disjunctive syllogism. My alternative approach should be seen as complementary to Berm´udez’s. For criticism of Berm´udez, see Lurz ().



The philosophy of animal minds

fork. This putative explanation is unsatisfying, because it cites an ad hoc mental module rather than a general mental capacity applicable in various environmental contexts. Strategy () satisfies all three constraints. I will develop an approach that satisfies the constraints while eschewing logical form and logical inference. My proposal is that Chrysippus’ dog performs a probabilistic inference over the space of cognitive maps. 4. bayesian reasoning over cognitive maps Bayesian decision theory is a formal framework for modeling probabilistic reasoning and decision-making. It represents a subject’s “degree of belief ” in various hypotheses through a subjective probability distribution p. Mathematically precise rules dictate how to update p in light of new evidence and how to act based upon p and one’s utilities. Given that p measures degree of belief, the question naturally arises: belief in what? It might seem that any adequate answer will cite sentences, propositions, or their ilk. Accordingly, philosophers often present subjective probability distributions as defined over logically structured entities. But Bayesianism is more general than this. It presupposes only an hypothesis space satisfying certain closure constraints. Elements of the hypothesis space must represent possible states of the world, but they need not have logical structure. In particular, they might be cognitive maps. Thus, we may posit a probability distribution defined not over logically structured representations but over cartographic mental representations. To illustrate, I will present a Bayesian-cum-cartographic model of Chrysippus’ dog. My treatment deploys ideas from probabilistic robotics. Indeed, one of my unofficial goals is to publicize this important field, which philosophers have largely ignored. I assume that Chrysippus’ dog (henceforth D) hunts its prey (henceforth X) by updating and consulting a probability distribution defined over the space of possible cognitive maps. When D reaches the crossroads, it recognizes three relevant possible states of the world, represented by  

I am indebted to Jos´e Luis Berm´udez for pressing this question. More precisely, the standard Kolmogorov axiomatization of probability defines a probability space as (
, A, p), where
is a non-empty set, A is a σ -algebra over
(i.e., a set of subsets of
that contains
and is closed under countable union and complementation in
), and p is a probability measure. It is consistent with this axiomatization to construe
as containing cognitive maps.



Chrysippus’ dog as a case study

three cognitive maps, M , M , and M , that correspond respectively to the following three concrete maps:

X

X

X

D

D

D

M1

M2

M3

M , M , and M do not purport to represent distances accurately, but they purport to capture relations of location and connectedness. More technically: they purport to capture topological properties but not metric properties. In this regard, they resemble subway maps. Since D initially lacks evidence regarding which path X chose, D’s initial probability distribution treats all three maps on a par: p(M ) = p(M ) = p(M ) = /. The probabilities sum to , because M , M , and M exhaust the space of possibilities. From left to right, label the three branches , , . D can sniff each branch i, an observation with two possible outcomes: yi , signifying that D detects some olfactory trace of X on branch i; and ni , signifying that D detects no such olfactory trace. Since these are the only two possible options, p(y i ) + p(n i ) = . I assume that D assigns conditional probabilities p(y i |M j ): the probability of measurement y i when D sniffs branch i, assuming that M j is veridical. Since D’s perceptual systems are fallible, p(y i |Mi ) is less than : even if X chose branch i, D may not smell it. To be conservative, I assume the “prior likelihood” p(y i |Mi ) = /. 

Some writers restrict the phrase “cognitive map” to representations that represent metric structure. However, this usage is hardly universal. For instance, Gallistel’s official definition, quoted in section , mentions “geometric relations” without privileging metric over topological properties.



The philosophy of animal minds

Since p(y i ) + p(n i ) = , it follows that the chance of false negatives is p(n i |Mi ) = /. There is also a slight chance of false positives, presumably less than that of false negatives. For i = j , I assume that p(y i |M j ) = /, and hence that p(n i |M j ) = /. As I explain below, one could vary these numbers considerably without altering the thrust of my analysis. I divide D’s activity into three stages. Stage one: D sniffs branch . Stage two: D sniffs branch . Stage three: D chooses branch  without sniffing. I now describe each stage in more detail. Stage one D sniffs branch , obtaining result n . How does this observation lead D to update p? Bayes’ law, a fundamental result of probability theory, asserts that: p(b|a ) p(a ) p(a |b) = . p(b) It is convenient to rewrite this formula as: p(a |b) = ηp(b|a ) p(a ), where we regard η as a normalization constant ensuring that relevant probabilities sum to . In the cases that interest us, Bayes’ law entails p(Mi |n  ) = ηp(n  |Mi ) p(Mi ). Intuitively: the probability of a given hypothesis, conditional on our evidence, is proportional to the prior probability of the hypothesis times the prior likelihood of our evidence conditional on the hypothesis. Substituting our assumed values for relevant probabilities, it is easy to show that p(M |n  ) = / p(M |n  ) = / p(M |n  ) = /. Following standard Bayesian procedure, we assume that D “conditionalizes”: as a result of observation n  , D updates its probabilities so that the

Chrysippus’ dog as a case study



new probability assigned to Mi is p(Mi |n  ). Thus, D redistributes probabilities over M , M , and M to /, /, and /, respectively. This is intuitively plausible. Since D did not detect any sign of X down path , D lowers the probability it assigns to M . No evidence yet differentiates between M and M , so D assigns them equal probability. Stage two D sniffs branch , obtaining result n  . How does this observation lead D to update p? We employ a generalized form of Bayes’ law: p(a |b, c ) = βp(b|a , c ) p(a |c ), where p(x |y , z) is the probability of x given that y and z obtain, and where β is a normalization constant ensuring that relevant probabilities sum to . Thus, p(Mi |n  , n  ) = βp(n  |Mi , n  ) p(Mi |n  ). Following typical practice in probabilistic robotics (Thrun et al. [], p. ), we deploy the Markov assumption: given the current state of the world, past observations are irrelevant to predictions about future observations. More formally, p(n  |Mi , n  ) = p(n  |Mi ). Under this assumption, it is easy to show that p(M |n  , n  ) = / p(M |n  , n  ) = / p(M |n  , n  ) = /. Assume that D conditionalizes once again. Then D assigns probabilities /, /, and / to M , M , and M , respectively. This is intuitively plausible. D’s two observations render M most probable, and they do not differentiate between M and M . Stage three So far, we have considered how D updates probabilities. We must now examine D’s utilities. I assume that D has four available actions: remain at the fork of the road (whether performing further observations or merely abandoning the chase); or else traverse branches , , or , respectively. Call these actions H, x , x , and x . Action u and map Mi jointly determine a



The philosophy of animal minds

new map, depicting what the world is like if it begins in the state depicted by Mi and then changes only in that D performs u. I denote this new map with “(Mi , u).” For instance, (M , H) is just M , while (M , x ) is

D

X

Note that, although I defined the metalinguistic term “(Mi , u)” by using the material conditional, the cognitive map denoted by this metalinguistic term does not itself have conditional structure, or logical form more generally. There are twelve maps (Mi , u), each associated with a payoff C(Mi , u). The following chart summarizes one possible set of payoffs: M1

M2

M3

H

0

0

0

x1

1

−1

−1

x2

−1

1

−1

x3

−1

−1

1

Columns correspond to possible initial states of the world. Rows correspond to D’s four possible actions. The entry in column Mi and row u is the payoff C(Mi , u) if map (Mi , u) is veridical. Our chart reflects two assumptions: D prefers catching X to not catching X; and D prefers remaining immobile to wasting resources in failed pursuit of X. The expected payoff E(u) of action u is a weighted average of the possible payoffs resulting from u:
p((Mi , u)|u) × C (Mi , u), E (u) = i=,,

where p((Mi , u)|u) is the conditional probability that map (Mi , u) is veridical given that D performs u. I assume that D’s actions generate no

Chrysippus’ dog as a case study



additional uncertainty, so that p((Mi , u)|u) = p(Mi ). Thus,
E (u) = p(Mi ) × C (Mi , u). i=,,

At stage one, E (H ) = , E (x  ) = E (x  ) = E (x  ) = −/. At stage two, E (H ) = , E (x  ) = −/, E (x  ) = E (x  ) = −/. At stage three, E (H ) = , E (x  ) = E (x  ) = −/, E (x  ) = /. Under the standard assumption that D performs the action with highest expected payoff, our analysis predicts that D performs H at stage one, H at stage two, and x  at stage three. This is the phenomenon we wished to explain. 5. a general theoretical framework Our Bayesian model of Chrysippus’ dog satisfies the three criteria from section . First, it explains the desired phenomena. Second, it supports appropriate counterfactuals. For instance, if X had chosen path  rather than path , then at stage two D would have recorded observation y  . It is easy to show that D would then have immediately chosen path . Third, our model depicts D’s behavior as reflecting a more general capacity to perform Bayesian reasoning over cognitive maps. That capacity extends far beyond the scenario described in section . Probabilistic robotics studies how robots equipped with the capacity to reason over cognitive maps can navigate through diverse physical environments (Thrun et al. []). It offers computational models describing how perceptual input induces a rational robot to update its probability distribution over a space of possible maps. Recently, robots along these lines performed impressively in the DARPA Grand Challenge Race, sponsored by the United States Department of Defense. Of course, current robotics algorithms are far more sophisticated than the tinker-toy model from section . But the basic ingredients are the same: probability distributions over cognitive maps, Bayes’ law, conditionalization, the Markov assumption, expected utility maximization, and so on. Thus, our simplistic



The philosophy of animal minds

model of Chrysippus’ dog illustrates a flexible theoretical framework that has already enjoyed great practical success. We could refine our model in various ways. For instance, we might consider maps that represent metric features of the environment, such as distances and angles. Or we might describe how D alters its heading as it sniffs different branches. Or we might describe D’s actions not in terms of their environment-involving consequences (e.g., D travels down the third path) but in terms of more specific environment-independent motor commands. Or we might describe D as updating its probability distribution and its motor commands even after it begins moving down path . Or we might treat D’s actions as introducing additional uncertainty, in which case we would abandon the assumption that p((Mi , u)|u) = p(Mi ). Contemporary robotics offers many ideas about how to effect these and other refinements. Although refinement might improve our analysis, it would not change the moral: we can build a robot that employs Bayesian reasoning over cognitive maps and that behaves as Chrysippus describes. The robot would also behave appropriately in counterfactual variants on Chrysippus’ scenario. 6. comparing the two explanatory strategies How might proponents of strategy () react to my discussion so far? I consider three possible objections. Objection: The model from section  is an instance of strategy (), not an alternative to it. Even if we grant that the model assigns probabilities and utilities to non-logical representations, the assignments employ identities, such as “ p(M ) = /” and “C (M , H ) = ,” that have logical structure. Moreover, the proposed model treats D as performing mathematical calculations, which requires logical inference from mathematical axioms. Thus, section  implicitly posits deductive reasoning over logically structured mental states. Reply: Identities such as “ p(M ) = /” and “C (M , H ) = ” describe which probabilities and utilities D assigns to which maps. But D itself can assign probabilities and utilities without employing these identities. The “assignment” consists in suitable functional relations between cognitive maps and mental representations denoting numbers. What is it for D to assign probability / to M ? It is for D to enter into a mental state bearing 

For empirical evidence that even fairly primitive animals such as rats represent and perform computations involving numbers, including non-integral rationals, see Gallistel (, pp. –). Note also that much of the scientific literature on perception treats low-level visual processes as performing

Chrysippus’ dog as a case study



appropriate functional relations to other mental states: the functional relations described in section . An assignment of probabilities or utilities to cognitive maps is a mental state that occupies a suitable role in probabilistic calculation. Nor does probabilistic calculation presuppose logical structure or logical inference. The literature offers numerous models of mathematical computation, such as Turing machines and register machines, that do not employ logical inference. These computational models demonstrate that mathematical calculation need not involve deducing theorems from axioms. Objection: The map-like character of cognitive maps plays no essential role in section . The same probabilistic calculations would apply if p were defined over unstructured representations, rather than representations with internal structure akin to concrete maps. Since Bayesianism rather than cartographic structure does all the work, section  illustrates nothing about the explanatory potential of cognitive maps. Reply: A model defined over unstructured representations fails to explain a crucial phenomenon: systematicity. As Fodor (, pp. –) emphasizes, a basic empirical fact about cognition is that creatures able to instantiate certain contentful mental states are necessarily able to instantiate certain other contentful mental states. For instance, it seems plausible that any terrestrial animal able to entertain the possibility represented by M could also entertain the possibilities represented by M or M . Fodor argues that a cognitive model based on unstructured mental representations cannot explain these systematic interrelations among possible mental states. He argues that a satisfying explanation should treat contentful mental states as relations to structured mental representations. We explain systematicity by noting that a representation’s parts can be recombined to form new representations. The model from section  implements this proposal, treating relevant mental states as relations to mental representations whose semantically relevant structure resembles that of ordinary concrete maps. As Braddon-Mitchell and Jackson (, p. ) note, ordinary cartographic representation is systematic, so a system of cartographic mental representations would likewise be systematic. I conclude that section  is far more satisfying than a model that replaces cognitive maps with unstructured representation.



sophisticated Bayesian computations (Knill and Richards []). Thus, the fact that my Bayesiancum-cartographic model attributes numerical computation to Chrysippus’ dog should not seem at all problematic. Editor’s note: See Tetzlaff and Rey (chapter ) for the argument that the best account of the systematicity of honeybee mental representations is that they are sentential (i.e., non-cartographic) in format.



The philosophy of animal minds

Objection: Section  relies on post hoc assumptions about D’s mental states. It assumes that D has winnowed the space of possibilities to M , M , and M . It assumes that D has selected appropriate prior probabilities – p(M ), p(M ), and p(M ) – and prior likelihoods – p(y i |M j ) and p(n i |M j ). It assumes that D associates various payoffs with various outcomes. Altering these assumptions would block the proposed derivation of D’s behavior or, even worse, generate an incorrect prediction. Since section  retroactively tailors its assumptions to the desired outcome, it yields a thoroughly vacuous account. Reply: The key question is how my account compares to strategy (). Presumably, proponents of () envisage something like the following explanation: D tracks X to the fork in the road, so D believes that X chose one of the three paths. D observes no olfactory signs that X chose either path  or path , so it concludes that X chose neither path. By logical reasoning, D concludes that X chose path . Since D prefers catching X to not catching X, D chooses path .

This explanation assumes that D believes itself to be facing a three-pronged fork in the road. It assumes that D believes X could have traveled down any of the three paths. It assumes that D can record relevant olfactory observations, which D then takes at face value as veridical. Finally, it assumes that D prefers catching X to not catching X. Altering any of these assumptions would block the proposed explanation of D’s behavior, or, even worse, yield an incorrect prediction. Our question is whether these assumptions are any less “post hoc” than those required by my treatment. In many respects, the assumptions seem comparable. The main apparent difference is that my assumptions are more specific. I assign precise numerical probabilities and payoffs, and I exploit my numerical assumptions in an essential way. Let us consider the various assumptions in turn. Prior probabilities: It is highly plausible that D initially assigns approximately equal probabilities to M , M , and M , since by stipulation D lacks any differentiating evidence. If desired, we could extend our model backwards in time, discussing how D generates an initial probability distribution over the space of possible maps. Mapping is a central research topic of probabilistic robotics, which offers a wealth of theories. Any such extended model should depict D as tending to assign roughly equal probabilities to M , M , and M until it collects relevant differentiating evidence. Prior likelihoods: My assumptions here are quite conservative. Specifically, the assumed high probability of false negatives – p(ni | Mi ) = / – shows that D can act appropriately even if it treats its own perceptual

Chrysippus’ dog as a case study



faculties as unreliable. Furthermore, we could vary my specific numerical assumptions considerably while generating the same predictions. Holding fixed the utilities and prior probabilities, and assuming that D treats the three paths symmetrically, we can easily show that any assignment of prior likelihoods yields the desired behavior if it satisfies these constraints: p(n i |Mi ) >  p(n i |Mi ) < p(n i |M j )/, where i = j. In other words, we achieve our desired result if D allows some probability of false negatives and if that probability is not too outrageously large. Payoffs: Once again, we can vary the specific numbers considerably while generating the same predicted behavior. Let a = C (Mi , u i ). If i = j, let b = −C (Mi , u j ). Let C (Mi , H) = . Holding fixed the prior probabilities and likelihoods, we can easily show that any values of a and b yield the desired behavior if b/ < a < b/. This inequality imposes a non-trivial constraint upon possible models of D. The constraint is a virtue, not a defect. It yields a quantitative analysis of how D’s payoffs and probabilities jointly determine action. For instance, if a < b/, then the prospect of wasted resources outweighs the prospect of catching X, so D remains immobile even while realizing that X probably chose path . In contrast, the explanation based on strategy () given above appeals vaguely to a “preference” for catching X. It offers no guidance in comparing that preference with D’s preference for conserving resources. Bayesian decision theory provides an appealing framework for conducting such comparisons, as illustrated by the above analysis. Apparently, then, section  is no less a priori plausible than the proposal advanced for strategy (). If so, then a satisfying treatment of Chrysippus’ dog need not cite logical reasoning over logically structured mental states. We can instead cite Bayesian reasoning over cognitive maps. 7. animal cognition Chrysippus’ dog is scarcely more than a thought experiment. To what extent does my discussion bear on actual non-linguistic creatures? Do such creatures perform Bayesian reasoning over cognitive maps? Do they execute deductive inferences? These questions, which transgress the limits of a priori analysis, impel us to leave the armchair and engage with scientific



The philosophy of animal minds

psychology. Thought experiments are useful. They facilitate comparison of alternative theories in a relatively clean setting. But they must eventually yield to actual experiments featuring quantitative measures and proper controls. In this spirit, let us first ask whether the approach sketched in sections – illuminates navigation. As already noted, many psychologists enthusiastically posit cognitive maps. Bayesian models are also popular within psychology, as applied to perception, word learning, and many other phenomena. A few researchers combine these two strands into cognitive models that posit probabilistic reasoning over cognitive maps. For instance, Balakrishnan et al. () argue that such models help explain various experimental results. This approach seems likely to receive further empirical application as its success within robotics becomes better known among psychologists. Of course, the particular model from section  is highly simplified. An empirically credible model would feature many refinements, including those mentioned in section . But the basic strategy looks promising. For further discussion, see Rescorla (in press a). What about logical reasoning? Many psychologists claim that nonlinguistic creatures perform deductive inferences. Building on work of Premack and Premack (), Call and Carpenter () presented a chimpanzee with two hollow tubes. As the chimpanzee watched, experimenters placed food inside one tube. A screen obscured which tube they selected. They then allowed the chimpanzee to search inside the tubes for the food. In – percent of the trials, a chimpanzee who discovered that one tube was empty immediately selected the second tube without searching inside it. Call () concludes that the chimpanzee employed disjunctive syllogistic reasoning to determine which cup contained food. Erd˝ohegyi et al. () report a similar but somewhat weaker result for dogs. In another series of experiments, Call () presented apes with two opaque cups, only one of which contained food. He then shook the empty cup so that the ape could observe that no noise was produced. Three out of nine test subjects performed above chance in selecting the unshaken cup. Call (, ) argues that these subjects executed a disjunctive syllogism. Each of the foregoing studies sought to disconfirm rival hypotheses through appropriate controls. For instance, Call () employed controls to show that his apes were not just smelling the hidden food or detecting inadvertent cues about its location from experimenters. He performed additional experiments designed to show that the apes’ behavior did not simply reflect a reinforcement history resulting in learned associations, such as an aversion to a noiseless shaken cup (Call [, ]).

Chrysippus’ dog as a case study



Nevertheless, many psychologists remain unconvinced. According to Penn and Povinelli (a), proponents of non-linguistic logical inference mistakenly assume a rigid dichotomy between associationist and deductivist theories of mental activity: either learned associations exhaust a creature’s mental activity, or else the creature performs logical inferences. This rigid dichotomy neglects the possibility of non-deductive mental processes vastly more sophisticated than associative learning. Penn and Povinelli caution that, even if we disconfirm an associationist explanation of some observed phenomena, we should not immediately embrace a deductivist conclusion. My approach, articulated in sections –, occupies Penn and Povinelli’s desired middle ground. It is neither deductivist nor associationist. Rather than positing logical inferences or learned associations, it posits rational probabilistic inferences over contentful mental representations. This approach can explain many behavioral phenomena supposedly indicative of non-linguistic syllogistic reasoning. For instance, the results from Call and Carpenter () and Erd˝ohegyi et al. () recall Chrysippus’ dog. We can readily explain those results through suitably altered versions of section ’s simplistic model. We treat the animal as updating a probability distribution over possible maps of its surroundings. The results reported in Call () are harder to accommodate, because they introduce a novel element: the relation between the cup’s contents, the shaking of the cup, and the noise thereby produced. We might accommodate this novel element through prior likelihoods p(n | M, s), where M is a map, s represents that a shaking of the cup occurs, and n represents that the ape’s sensors detect noise. But this maneuver substantially alters the model from section , in which prior likelihoods are conditional only upon cognitive maps, not upon spatiotemporal events (such as a shaking of a cup). An adequate theory of how the ape arrives at a suitable prior likelihood p(n | M, s) will go substantially beyond the models of map-learning and navigation currently employed within probabilistic robotics. What additional representational and inferential resources beyond those posited in section  would an adequate theory require? According to Call, his results show that the ape represents and reasons about causal relations. Penn and Povinelli (a, pp. –) disagree. But suppose we concede that Call is correct. In particular, suppose we concede that the prior likelihood p(n | M, s) reflects the ape’s grasp of causal relations among physical events. We do not thereby concede that the ape engages in anything resembling logical reasoning. There is no obvious reason why a systematic



The philosophy of animal minds

theory of causal representation and reasoning must invoke logically structured mental states. For instance, consider the theory of causal Bayes nets (Pearle []). A causal Bayes net is a directed acyclic graph. Each node is a variable whose values represent possible events or states of the world. A directed edge from one node to another represents direct causal influence of the former upon the latter. Every node is associated with a subjective probability distribution, conditional on the values of its parents. Causal influence and probabilistic dependence relate through the causal Markov Condition: a node is probabilistically independent of its nondescendants, conditional on its parents. Under the Markov assumption, conditional probability distributions for individual nodes determine a unique joint probability distribution defined over all the nodes. This formalism yields an elegant framework for representing and reasoning about causal relations among events. Working within the framework, researchers have proposed various algorithms for inferring causal relations from observed correlations. Although the framework originated in statistics, computer science, and philosophy, several cognitive scientists have recently deployed it within empirical theories of causal reasoning in humans (Gopnik and Schulz []) and non-humans (Blaisdell et al. []). To my knowledge, no one has yet integrated cognitive maps and causal Bayes nets into a synthesized theory of map-learning, navigation, and causal reasoning. But I see no obvious bar to an integrated theory. Since neither element of the proposed synthesis requires logically structured representations or deductive inferences, I see no reason why the proposed synthesis would require those resources. Clearly, the topic deserves further investigation. Although my discussion of causal reasoning may seem intolerably vague, I have offered more detail than psychologists who attribute logical reasoning to non-linguistic animals. As Penn and Povinelli note, such psychologists never provide or even gesture towards formal models. For instance, Call offers only vague folk-psychological talk about “causal-logical reasoning,” without hinting how to convert such talk into actual psychological models. Thus, my proposal is no vaguer than Call’s. I submit that many experimental results supposedly indicative of nonlinguistic deduction can be explained without citing logical reasoning or logically structured mental states. Admittedly, I have addressed only one strand in the relevant psychological literature. A more thorough discussion would survey the many other experimental results that purportedly

Chrysippus’ dog as a case study



reflect non-linguistic deduction. Nevertheless, we can draw some preliminary conclusions. First, rational psychological processes defined over cartographic mental representations are possible. The relevant processes, grounded in Bayesian decision theory, differ markedly from deduction. Second, behavioral phenomena that superficially appear to involve logical reasoning may instead reflect non-deductive mental processes defined over non-logical mental representations. Hence, we must exercise caution when arguing through anecdotes, thought experiments, or scientific experiments that non-linguistic creatures’ mental states have logical form. Whether nonlinguistic cognition features logically structured mental states is an open question, to be settled through sustained engagement between philosophy and scientific psychology. 

For critical discussion of relevant psychological literature, see Allen (), Berm´udez (a, pp. –), and Penn et al. ().

chapter 4

Systematicity and intentional realism in honeybee navigation Michael Tetzlaff and Georges Rey

1. introduction Do animals really have intentional states, or is intentional ascription merely a convenient instrument for predicting their behavior that involves no commitment to the reality of those states? One way to give substance to this question is to consider the recent debate between “classical” and “radical connectionist” approaches to cerebral architecture. According to the classical, realist theory of cognition, championed by Fodor (, ), cognitive processes consist of computations defined over causally efficacious, syntactically specified representations, for example sentences in a “language of thought,” whose syntactic structure preserves the content of those representations compositionally: representations are either simple or complex, the complex ones being composed by concatenation of the simple ones in such a way that the semantic properties of the complex are a function of the semantic properties of the simple. The main rival to classicism is (non-implementational, or “radical,” “distributed”) connectionism, which proposes instead that cognitive processes are computations defined over syntactically simple, distributed representations, for which the constituency relation is certainly not concatenative, nor standardly compositional. Indeed, in contrast to classical machine architectures, connectionist architectures standardly do not make available to an organism recombinable representations that might be stored in memory and deployed at different times for different tasks. This air of unreality about internal representations can lend support to the aforementioned instrumentalism, as in Dennett (, a).



This essay is largely a distillation of Tetzlaff (). The lines are not always drawn quite so sharply; see, e.g., Horgan and Tienson () and McLaughlin () for discussion.



Systematicity and intentional realism



The main literature debating these issues (e.g., Fodor [], Fodor and Pylyshyn [], Marcus [], and Smolensky []) has focused largely on human beings and the apparent systematicity of their thought. As we will understand it, systematicity may be characterized thus: (SYS) Ceteris Paribus: x is sensitive to the content [p] iff x is sensitive to F[p],

the square brackets indicating the content of the expressions they enclose (see section .), and “F[p]” any permissible, formal permutation of [p]. The most obvious case of syntactic structure is logical structure. Thus, someone understands [If Bill goes, then Ann or Jill stays] iff she understands, for example, [If Ann stays, then Jill or Bill goes]. An example closer to the bee examples we will discuss is: x understands [Ann is north of Bill] iff x understands [Bill is north of Ann]. Fodor () argues that this condition is patently satisfied by normal, linguistically competent adults. Fodor’s discussion focused, however, largely on human linguistic competence. This unfortunately confounded the discussion with issues about the systematicity of natural language, which, arguably, provides a special medium for peculiarly human thought (see Dennett [a]). We propose to control for this confound by considering the recent literature on honeybee navigation. After a brief discussion of some background issues (section ), we will summarize some of the substantial research on honeybees’ remarkable abilities to navigate and to convey information about various resources to other bees by means of their “waggle dance” (section ). We will argue (section ) that an examination of those abilities reveals that the processes underlying them are systematic, that this systematicity is best explained by presuming that honeybees implement some sort of classical language of thought (section ), and that this explanation needs to be understood realistically (section ).





The formal permutations are, of course, of the natural language expressions that express the propositions. We assume that a law like (SYS) is intensional (with an “s”), relating properties consisting of these propositions, and thus sensitive to how those propositions are expressed, but that nothing in the present discussion depends upon working out the details of this (we will discuss other features of (SYS) in section .). Fodor and Pylyshyn () occasionally allude to non-linguistic cases, but do not develop them in detail.



The philosophy of animal minds 2. background philosophical issues 2.1. Intentional content

Claims like: () The bees learned that the feeder is m east of the hive are common in the literature on bee and insect navigation generally. Taking such claims at face value, we will assume what they presuppose, that the relevant behavior of bees requires intentional explanation (i.e., explanation in terms of some sort of representations of “information” – e.g., regarding properties, objects, and locations – that is at least indirectly indicated in English by “that . . .” clauses towards which organisms bear some or other “propositional attitude”). Following philosophical practice, we will refer to such “information” as intentional content, which we designate by enclosing expressions that express that content in square brackets (e.g., “cats snore” expresses the content [Cats snore]). We will avoid the more common rubric of “meaning” in view of its entanglement with issues of natural language and speech pragmatics; and when we do use the term “information” we intend it as synonymous with “intentional content,” without its sometimes misleading association with the purely physical notion (roughly, negative entropy) that goes by the same name, and, so far as we can see, has little to do with the present discussion. Other than claiming that intentional content is what representations are “representations of,” we have no good definition or theory of it, and suspect no one else so far has either. Of course, a problem of enormous significance and difficulty for psychology in general is how to characterize precisely the intentional content of representations. We suspect that any expert on bee cognition would admit 







N.b., the objects or properties may or may not be real. We want to leave it an open question whether bees actually do succeed in representing actual objects in real space, a controversial issue between “internalists” and “externalists” about intentionality; see G. Evans (), Fodor (), and Rey (). We remain completely neutral about just what these attitudes might be (e.g., whether they are “beliefs” and “desires” or perhaps more technically defined attitudes, such as “cognize”). There is, of course, no presumption that the attitudes are the least bit conscious. Dretske () and Fodor () have tried to introduce notions that look to both the physical and the intentional notions, and it is certainly not implausible to suppose (what we think researchers routinely suppose, see Gallistel []) that some intentional content is determined by some sort of “informational,” covariation relation. However, precisely how to specify that relation is open to considerable controversy (see Loewer []), and so we shall not presuppose such views here. Pace Quine () and his followers, we do not see this as an occasion for despair. Plenty of notions have proved indispensable to science despite resisting clear definition for centuries – consider the notions of “limit” and “infinitesimal”!

Systematicity and intentional realism

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that there is a sense in which () could be true, even though the precise content of the bees’ representations would not be [the feeder is m east of the hive]. Bees, after all, probably do not entertain representations with the specific content [feeder] or [m]. So how are we to understand content attributions such as those involved in such claims? We do not pretend to have any adequate answer to this question. Indeed, we think the answer can only be approached by considering in ever finer detail the work ascriptions of content will do in explaining phenomena like that of perception, reasoning, decision-making, and animal navigation, such as that of bees, along lines we pursue here. It will help, however, to note a distinction between so-called “de re” and “de dicto” ways of reporting (or ascribing) intentional states. A dog might bark at the mailman, and, to a first approximation, that might be explained by saying that the dog thought the mailman was a threat. But this would not ordinarily be taken to imply that the dog really represented the mailman as a mailman, that the dog had the remotest concept or conception of the man as a carrier of mail. The dog was presumably just barking at the man who we, the ascribers, know to be the mailman. Such is a de re (or “of the thing”) ascription, as opposed to a de dicto (or “in those very words”) one, which would attempt to characterize the actual way the dog, itself, represented the man. But, of course, this latter is, at this stage of our knowledge, immensely difficult to determine. We will not attempt to determine such questions in the case of the bees, but will by and large restrict ourselves to what we take to be the de re ascriptions intended by the researchers in the area. And, even then, our descriptions (as those of the researchers) must be taken tentatively, since even saying exactly what actual thing (if any) – a hive, a foraging site, the azimuth of the sun, optic flow, an angle number – a bee might be representing may not be easy to determine. For example, it is possible that bees do not represent their hive per se. Rather, they may represent only various parts or features of it, while lacking a representation of the entire structure. A proffered object or property may be a sub-instance of a larger category to which the bee is sensitive: perhaps it is not [hive], but maybe [oval solid]. It is even possible that the extensions of many or all bee mental-representational constituents do not include anything external to bees at all: they could turn out to be “lucky” (though not accidentally successful) hallucinators (cf. fn. ). One  

Consequently, we will not be committed to whether the relevant intentional content is “conceptual” or not. Issues of error of course raise the “disjunction” problem discussed by Kripke () and Fodor (), which we mercifully will not address here. See Pietroski and Rey () for a way of dealing with it consonant with the present discussion.

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The philosophy of animal minds

does the best one can. We do think, however, that our conclusions about systematicity will not turn on determining at this point precisely which of alternative de re or de dicto ascriptions are appropriate: we think bees’ sensitivities are systematic whichever of them (within plausible bounds) is correct. 2.2. Features of (SYS) A number of features of (SYS) bear emphasis: (i) As a ceteris paribus law, it is open to apparent exceptions, especially those due to the interference of independently identifiable factors, such as distraction, fatigue, or worldly anomaly (perhaps it is too preposterous to think that Ann stays). We assume the issues here are typical of macro-scientific laws (cf. Pietroski and Rey []), and afford a reply to the apparent exceptions raised by, e.g., Dennett (a) and Kaye (). (ii) As a law, (SYS) presumably supports counterfactuals that are entailed by it. Thus, a system is not systematic if it just happens to be sensitive to F[p] iff it is sensitive to [p]. The proverbial primates randomly typing on a typewriter are not systematic simply because, in time, they will produce “F[p]” iff they produce “p.” (This feature is relevant in considering defenses of connectionist rivals to classicism; see section  below, and Fodor and McLaughlin [].) (iii) We do not address here the question of precisely how systematic honeybee cognition is overall. Perhaps some cognitive systems are modularized (see Fodor []; Carruthers []) and representations from one module cannot be permuted with representations from another. Or perhaps systematicity breaks down for bizarre possibilities: a dog might be able to represent [there is no water in the dish or people are in the room], but have trouble with [there are no people in the dish or water is in the room] (cf. Kaye []). Our claim will be merely that there is a significant domain in which (SYS) is true for bees. 3. the honeybee data and systematicity In the next nine sections, we will describe some of the most relevant experiments and data for establishing the reality of classical representations in honeybees, and argue that they have systematically related navigational capacities. 

For rich, recent reviews of the data on honeybee psychology, see Giurfa () and Menzel and Giurfa ().

Systematicity and intentional realism



3.1. The waggle dance Perhaps the best known and most remarkable fact about bees is their waggle dance, about which the following seems to be true (modulo the aforementioned difficulties of intentional ascription). Bees perform this dance in front of other bees upon returning to their hive after their foraging expeditions for various useful items (e.g., nectar, pollen, resin, water, and potential new nest sites). The dance encodes the relative location of their hive and a recent foraging site in terms of the distance and direction between them. The bees estimate distance flown primarily by monitoring optic flow, or image movement across the retina (Dacke and Srinivasan []), and they estimate direction principally by means of their solarcompass mechanism (see section .). Dances consist of multiple, straight waggle “runs,” followed alternately by a clockwise or counterclockwise loop back to the start of the next run. The duration of the run corresponds to the distance to the site, and the angle of the run with respect to gravity corresponds to the site’s direction relative to the current solar azimuth (the compass direction of the sun) (Dyer []). For example, a vertical run indicates that the direction to the site is toward the current azimuth; a run ◦ right of vertical indicates that the direction is ◦ right of it. Recent experiments have shown that the information encoded in waggle dances is effectually transferred to the bees that respond to them (Riley et al. []). It is important to note the cognitive difficulty confronting a bee producing or decoding a dance. Apis mellifora foragers dance in darkness on the vertical surface of a hive comb. The dancer has to “translate” orientation with respect to solar cues, no longer available in darkness, into orientation with respect to gravity. Moreover, they are able to do this often hours or days later than their original foraging flight, correcting the dance to allow for the intervening changes in the solar azimuth (see Lindauer []). The bees that respond to the dance have to decode this updated dance, determining where to fly. It is hard to underestimate the significance of these facts: they seem to require the bees to be able to integrate and access highly specific information about spatial relations, and translate it from one mode of presentation (dances oriented to gravity) to another (representations of the solar azimuth), with the former being carefully updated to keep track of the latter. On the face of it, it is hard to see how they could accomplish this without highly articulated representations of space and of various objects (a site, a hive, the sun) within it, representations that, moreover, would appear to have constituent structure, as we think the following facts about their abilities reveal.



The philosophy of animal minds 3.2. Vector navigation

The ability to navigate by path integration is ubiquitous in animals from insects to humans (Gallistel []). Path integration is the process by which an animal continually updates the current distance and direction from its present location to its departure point, solely by monitoring its angular and linear displacements. Such a process explains a wide number of phenomena observable in bees: their ability in featureless terrain to home directly toward the hive after performing a circuitous flight (Chittka et al. []); their ability to retain a course over featureless terrain after having been blown off it by strong crosswinds; and their performing extensive search flights, periodically returning to the origin of the search flight, even in the absence of sensory cues marking its location (Reynolds et al. []). It also explains how the waggle dances of foragers trained to follow a detour around a large obstacle nonetheless indicate the straight-line direction from the hive to the foraging site. The extent to which honeybees rely on distance and direction information is clearly manifest in their performance of vector flights. Harmonicradar experiments have shown that honeybees trained to a single established feeding station will navigate by relying on learned vector information, tending to disregard landscape cues (Riley et al. []; although, as we will see, they do rely on these sometimes as well). After such training, honeybees captured at the feeder after filling their crops, and soon released at an unfamiliar location, will fly a vector that would have taken them from the feeder to the vicinity of the hive had they not been displaced. They will do so despite the presence of conspicuous landmarks not present along their trained route. The converse also holds: if the bees are captured when preparing to depart from the hive, then, once released at an unfamiliar location, they will fly a vector that otherwise would have taken them from the hive to the vicinity of the feeder. A good example of vector integration is afforded by “shortcut” behavior, which Menzel et al. () have observed bees exhibit between familiar locations other than the hive. Harmonic radar tracked the bees over the entire course of their flights. One group of bees was trained to a feeder situated m east of the hive. They were captured at the feeder after they filled their crops, and released at a distant location. They then performed a vector flight that otherwise would have led them to the hive. Having unexpectedly arrived somewhere other than the hive, and finding themselves lost, they began an extensive search. Eventually, the bees encountered a familiar landmark, the location of which, in relation to the hive, they had previously learned (presumably by path integration) while exploring the

Systematicity and intentional realism



experimental area. Many of the bees then flew directly to the hive, even though they may not have previously flown directly to the hive from the relevant landmark. And many of the bees flew to the feeder instead by a completely novel route! Furthermore, no sensory cues were available to the bees that would have enabled them to locate the feeder from the location of the landmark on the basis of previous learning. Apparently, these bees recalled both the distance and direction to the hive from the familiar landmark, the distance and direction of the hive to the feeder, and integrated those memories in order to set their course. 3.3. The solar ephemeris As we mentioned, honeybees compute direction by exploiting the solar azimuth, which they use as a reference point in order to set and hold a compass course. They are also able to use the sun-linked pattern of polarized light in blue sky to locate the azimuth when the sun is blocked from view. Because the sun moves in relation to the landscape, a bee’s returning to a familiar site at different times of day requires its flying at different angles in relation to the azimuth, so it must be able to estimate how much the azimuth changes during the relevant time spans. This in turn requires the organism to be informed about the time of day (information provided by its circadian clock), and to have a record of the solar azimuth as a function of time of day. Such a record is called a solar ephemeris. The solar ephemeris varies with time of year and latitude. Hence, although sensitivity to the solar azimuth seems to be innate (see Dyer and Dickinson []), the current ephemeris for a particular locale must be learned. Complicating matters is the fact that the rate of change of the solar azimuth varies with time of day, and the fact that the solar ephemeris must be learned quickly, since a honeybee lives on average for only about ten days after it becomes a forager, making few exploratory flights before beginning to forage. The solar ephemeris learning mechanism produces a record that allows bees to estimate fairly accurately the azimuth of the sun at times when they are not seeing it, due to heavy overcast, or have not ever seen it, due to controlled, limited exposure (Dyer and Dickinson []). Perhaps most remarkably, their solar ephemeris allows bees to estimate the solar azimuth at night – and this is true not only for nocturnal, but also diurnal, foragers. Dyer () observed the waggle dances of Apis dorsata after night flights to known foraging locations. Some of the recorded dances occurred about three hours after sunset, some before dawn, and both fairly accurately indicated the actual solar azimuth (the accuracy at night seems to be less



The philosophy of animal minds

good). Lindauer () trained a colony of diurnal foragers to feed at a station to the south during the one-hour period after sunrise and to feed at a station to the east during the one-hour period before sunset, and then stimulated the bees to perform waggle dances at night. Apart from a few hours around midnight, the dances approximately indicated the solar azimuth. 3.4. First systematicity hypothesis We will now propose our first systematicity hypothesis. Crucially, the solar compass mechanism, which allows bees to represent hive-to-site solar bearings, also allows them to represent site-to-hive solar bearings. It provides them with a general capacity sufficient for the possession of numerous, more specific capacities, such that possession of that general capacity, together with one of the specific capacities, nomologically guarantees possession of the others. Thus, for any solar bearing α, bees have the capacity to represent the solar bearing from a particular familiar site to the hive, when that bearing is α iff they have the capacity to represent the solar bearing from the hive to that site, when that bearing is α; that is: Ceteris paribus: A honeybee is sensitive to the content [the solar bearing of foraging site S from the hive is α] iff it is sensitive to the content [the solar bearing of the hive from foraging site S is α].

Our case for this hypothesis does not rely solely on the apparent nature of the honeybees’ solar ephemeris. Thus, a feeder at a fixed distance from the hive could be slowly moved to a place ◦ from its original one, the bees all the while updating their representations of the feeder’s location (see section .). 3.5. Landscape cues Neither bees nor other animals rely on path integration alone. Small path integration errors accumulate, so it is quite useful to be able to compare and perhaps correct information provided by the current output of the path integrator with information about the previously learned locations of stable landmarks. It is also quite useful to be able to override default reliance on learned vector information (section .). Instead of just flying off willy-nilly on the basis of where you think you are, it might pay to verify where you are first. 

For more on nocturnal dances, see Edrich (), von Frisch (), and Lindauer ().

Systematicity and intentional realism



In fact, when surreptitiously displaced and released, honeybees do not always fly the vector they otherwise would have flown, but instead rely on familiar landscape features in order to set their course. Menzel et al. () introduced honeybees to an unfamiliar area and trained them to forage at two distant feeding stations: at one, M, southeast of the hive, only in the morning, and at the other, A, northeast of the hive, only in the afternoon (see diagram ). H (Hive)

A (afternoon feeding station) R (release site) M (morning feeding station) Diagram 

R is a release-only site, east of the hive, situated between the feeding sites. Bees were captured either at the hive, H, after arrival from one of the feeders (hive-arriving bees), at the hive when preparing to depart to the timeappropriate feeder (hive-departing), upon arrival at one of the feeders, or while preparing to depart from one of the feeders. They were then displaced to one of the other sites, A, M, or R, where they were released. Bees of every group that were captured in the afternoon and released at M were able to fly toward the hive upon release. The hive-arriving bees and hivedeparting bees captured in the morning and released at A also flew toward the hive upon release. These bees seem to have been able both to recognize their location of release and to recall the appropriate homeward direction: instead of flying along the vector they otherwise would have flown, they relied on familiar landscape features in order to set their course. This is particularly significant in considering the richness of the representational system the bees must use, since it must perforce be one that is capable of integrating the aforementioned representations of direction and distance with arbitrary information about things or properties at various locations at various times. 3.6. Conflicting information about the same place These results of Menzel et al. () illustrate another important feature of bee navigation, the ability to refer to the same place and resolve conflicting information about it. This is not an ability that a creature comes by automatically by merely having two representations that as a matter of 

This ability is also manifest in the way bees update their information about the location of a familiar site in relation to local landmarks (see Wei et al. []).



The philosophy of animal minds

fact refer to the same place. After all, they might represent hive- as a certain point in a vector integration and hive- as something to the left of a tree, and not represent hive- as identical to hive-, even when it is. However, as we are about to argue, it would be difficult to explain the actual navigational abilities of bees if we could not assume that they are able to integrate information about the same place as the same place, resolving conflicts that might arise in that information. Menzel et al.’s results suggest that the displaced bees represented the two foraging sites and the hive in the same way in which they represented them during previous foraging excursions after they had filled their crops. Neither their having just flown to the foraging site nor their having fed there (or fed at all) was necessary in order for them to call up the appropriate homeward bearing. Likewise, when released at previously unvisited Site R, both hivedeparting bees and hive-arriving bees were able to adopt the novel R-to-H flight-path, regardless of time of day. As Menzel and colleagues argue, these novel-shortcut bees seemed to recall both site-to-hive directions and “averaged” them to obtain the novel R-to-H direction (see Tetzlaff [] for discussion). Thus, those bees must have represented the hive and the two known sites in the same way in which they had on previous foraging excursions. Otherwise, it would be hard to see how they could treat both (or either) of their learned site-to-hive directions as relevant to the task of returning to the hive from R. Bees can also resolve conflicting information about what to do at particular places. For example, Zhang et al. () demonstrated that honeybees can be trained to reverse their choice preference, both at the hive and at the feeder, depending on time of day. In one experiment, the same bees learned both (i) to choose, in the morning, a black–white “sector” (or “pinwheel”) pattern, over a black–white concentric rings pattern, at the feeder, and then to choose a black–white vertical grating over a black–white horizontal grating upon arrival back at the hive, and (ii) to choose, in the afternoon, the rings pattern over the sector pattern, at the feeder, and then to choose the horizontal grating over the vertical grating at the hive. In another experiment, bees learned both (i) to choose, in the morning, a horizontal grating over a vertical grating at the feeder, and then to choose a vertical grating over a horizontal grating at the hive, and (ii) to choose, in the afternoon, the vertical over the horizontal grating at the feeder, and then to choose the horizontal over the vertical grating at the hive. Bees appear to have a general ability to vary parameters in a pattern independently of any specific task.

Systematicity and intentional realism



3.7. Second systematicity hypothesis The capacity of bees to reverse their choice preferences, presumably over a wide range of stimulus cues, provides us with a family of systematicity hypotheses, such as: Ceteris Paribus: A honeybee is sensitive to the content [Choose A at the feeder and B at the hive] iff it is sensitive to the content [Choose B at the feeder and A at the hive].

Thus, provided that a bee can learn to choose A over B at one time while performing a certain task at a certain place (e.g., returning home in the morning), it can learn to choose B over A at another time while performing that same task at that same place. Furthermore, it seems that the bees would represent the relevant place (e.g., hive) as the same place, regardless of time of day and the choice to be made. 3.8. Sequence learning The generality and richness of the honeybee’s navigational abilities is further manifested in its ability to remember various kinds of sequences per se, as opposed to the capacity merely to remember different items recalled sequentially via trigger-response mechanisms. Honeybees have the capacity to remember a sequence of flight vectors like the following: first, fly n distance units in direction d; second, fly m distance units in direction d∗ (T. S. Collett et al. []). The same study also showed that bees have the capacity to learn a sequence of positive visual stimuli, in the process of learning to negotiate a simple maze. Thus, in order to negotiate the maze, bees learned to choose, for example, the hole marked with white rather than the one marked with black in the first chamber, the hole marked with blue rather than the one marked with yellow in the second chamber, and the hole marked with vertical stripes rather than the one marked with horizontal stripes in the third chamber. M. Collett et al.’s () study on the effects of panoramic context on the performance of route flight segments showed that honeybees have the capacity to learn route sequences of the form: first, fly distance n to landmark L, then fly distance m to landmark L∗ . For each of these cases of sequence learning, alternative hypotheses to the effect that the bees merely recalled separate memories in the correct order can be ruled out (see Tetzlaff [] for discussion).



The philosophy of animal minds 3.9. Final systematicity hypotheses

Honeybees presumably have the general capacity to represent a wide variety of each of the above three types of sequences. These general capacities yield the following systematicity hypotheses: Ceteris Paribus: A honeybee is sensitive to the content [Fly distance n in direction d, then fly distance m in direction d ∗ ] iff it is sensitive to the contents [Fly distance n in direction d ∗ , then fly distance m in direction d] and [Fly distance m in direction d ∗ , then fly distance n in direction d]. Ceteris Paribus: A honeybee is sensitive to the content [Fly distance n to landmark L, then fly distance m to landmark L∗ ] iff it is sensitive to the contents [Fly distance m to L, then fly distance n to L ∗ ], [Fly distance n to L∗ , then fly distance m to L], and [Fly distance m to L∗ , then fly distance n to L]. Ceteris Paribus: A honeybee is sensitive to the content [Choose white in the first chamber, blue in the second, vertical in the third] iff it is sensitive to the contents [Choose white in the first chamber, vertical in the second, blue in the third], [Choose blue in the first chamber, white in the second, vertical in the third], and so on.

All of the various instances of systematicity we have proposed support the generalization: (BEE-SYS) Ceteris Paribus: For a range of navigational capacities, a honeybee is sensitive to the content [p] iff it is sensitive to F[p].

To reiterate a point we made at the start (section .), the truth of our various systematicity hypotheses does not depend upon settling just which proposals about the precise content of bee states is actually correct. The candidates we have provided are simply the plausible de re ascriptions spontaneously provided by the researchers. Note that systematicity does not imply productivity, or the ability to recursively generate a potential infinitude of expressions from a given base. Indeed, for all their ability to permute representations into novel ones, it appears that no non-human animal has any such recursive ability (see Hauser et al. []). At any rate, there is so far no evidence that a bee that can represent a sequence can also represent indefinitely long repetitions of such a sequence, in the way that even small children spontaneously can do.

Systematicity and intentional realism



4. classical vs. connectionist proposals There is a straightforward explanation of systematicity in terms of classical cognitive architecture. A classical system is sensitive to the content [p] iff it is sensitive to F[p] for two reasons. First, the vehicles expressing the contents [p] and F[p] have the same syntactic structure and the very same constituents: the syntax-sensitive mechanisms that would be responsible for tokening [p] are the very same as those that would be responsible for tokening F[p]. Second, the contents [p] and F[p] are determined by their vehicles’ syntax and by the contents of their vehicles’ constituents. Of course, it is possible to rig a classical system so that its capacities are not systematic (cf. Aizawa []). But it is important to see that rigging would be required. For a proportion of the possible values of “p” (consider whatever proportion you think would warrant the label “asystematic”), to devise a classical system that is sensitive to [p] but not sensitive to F[p] would require permitting entokenings of representations of [p] while blocking entokenings of representations of F[p]. That, however, would prevent the formation of such a large proportion of syntactically well-defined representations that the system would hardly seem to be appropriately classified as a syntax-driven mechanism, contrary to what classicism requires. Thus, it is asystematicity, not systematicity, that has to be specifically designed into classical systems. On the other hand, we do not have a good idea of how an explanation of systematicity could proceed in terms of non-classical architectures. Such an architecture could, of course, produce F[p] iff it produced [p], but only as a by-product of producing all other patterns, well- or ill-formed, as well: the systematicity would not be a law (see section . (ii) above). Such architectures, by design, do not support concatenative syntax. So an explanation of systematicity in terms of such an architecture will have to appeal to something other than such syntax. Smolensky () has shown in principle how a connectionist architecture could exhibit systematicity. His account requires positing (among 

Particularly in considering navigation, it is almost irresistible to talk about creatures using some kind of “cognitive map.” We think it is unlikely in the extreme that this talk can be taken at face value, since it seems to us unlikely that there is literally a map in the brain over which computations are defined. There might, of course, be in the bee’s brain isomorphisms between, say, stimulated portions of a visual ganglion and figures in ambient space. But what needs to be shown is that these isomorphisms are exploited computationally (e.g., the actual spatial properties of the representation are used to represent properties of what is represented, and are essential to the success of the computation – see Rey [] and Pylyshyn [, ch. ] for discussion). [Editor’s note: see Rescorla (chapter ) for a dissenting opinion on the reality of cognitive maps in animals.]

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The philosophy of animal minds

other things) linearly independent syntactic-role vectors (ordered n-tuples of activation values) that bind (superimpose) with other representational vectors, a binding that provides for a sort of functional, non-concatenative syntax (Horgan and Tienson []). However, this sort of architecture is capable of exhibiting systematicity only if it is provided with the capacity to entoken such syntactic-role vectors. For ultimately, syntactic-role vectors must be entokened along with vectors that represent individuals or properties in order for them to be superimposed. But then not only must the capacity to exhibit systematicity be specifically built into such an architecture, but also it must be capable of implementing a classical language of thought after all. Likewise, all such vectors must be entokened in order for the system to perform operations on its representations, particularly if, as we have seen, bees are able to access and update information about specific parameters across a wide variety of tasks. Of course, Smolensky argues that none of this is so, since, he claims, weight matrices would be available that can process vector representations without their constituents having to be entokened. But we think that this is precisely what he has not established. The general lesson is this. Representationalists of any stripe who reject classicism need to find a way to make their proposed cognitive architectures behave as if their representations have concatenative structure, even though they do not. Such theorists either will not succeed, or they will only appear to succeed either by inadvertently implementing classical representations or by special rigging (cf. Hadley []). Such “success” may come, but only at the cost of explanatory power. 5. instrumentalism Why suppose that honeybees acquire informational content at all? Why not suppose that attributing content to bees may be nothing more than our adopting an “intentional stance” (Dennett []) or other “interpretive” 



Indeed, to take an analogy that Smolensky (, p. , n.) himself has drawn, it seems as improbable that one could implement bee systematicities and information integration without entokening constituent structure as it would be that one might prove theorems in logic by G¨odel numbers without factoring the numbers into their primes. Note, though, that the presence of such implementation might not be obvious: the system might seem to be radical connectionist on the surface, but it could (indeed, arguably must) nonetheless implement a classical structure. To insist that there is no such classical implementation requires showing a negative existential, which, notoriously, it is by no means easy to do. Editor’s note: see Rescorla (chapter ) for a representational account of the concatenative structure of cognitive maps.

Systematicity and intentional realism



kind of understanding (Davidson []) toward their behavior, whereby they are regarded as “rational,” but about whose actual truth there is no fact of the matter? Perhaps what we see as the systematic nature of bee cognition lies merely in our interpretation of the data rather than in any real mental capacities of bees. Consider, however, waggle dances (section .), path integration, vector flights (section .), and the solar compass mechanism (section .). What is striking here is that there is no question of the bees’ overall rationality – certainly nothing remotely approaching their appreciating the deductive closure of their “beliefs” (Dennett [], p. ). As we’ve noted, their representations, like those evidently of most non-human minds, don’t seem productive or recursive. Indeed, our ascription of intentional content to their states, albeit provisional (section .), is not based on any “norms” of rationality, but rather upon observation of a number of lawlike relations that obtain among, for example, the distances and compass directions of foragers’ flights to particular sites, the durations and orientations in relation to gravity of their post-flight waggle runs, and the distances and compass directions of the flights of bees recruited by those foragers’ dances. Moreover, as the phenomenon of default reliance on recalled flight vector information shows, these relations can obtain independently of the current landscape cues encountered during flight – they irrationally ignore what’s right in front of their noses. For all that, however, honeybees seem to be able to perform vector-algebraic computations on the relevant information (sections ., .), integrate this information with information about features of the landscape (section .), and resolve conflicts in information about the same place (section .). These are precisely the kinds of phenomena that are explained in terms of computations over stored and retrieved representations. It is unclear, to say the least, how one might proceed without them. Unless the interpretivist is prepared to be an instrumentalist about all explanation, it behooves him to say why the instrument works in the specific way it does, and in this case it is hard to see how it could do so without the states it postulates being real. 

It might be claimed that they obey some sort of practical, decision-theoretic rationality. However, other than assuming in many of these cases that they navigate towards things they more or less “want,” we have absolutely no evidence that they, for example, always navigate so as, e.g., to maximize expected utility. Perhaps some of the navigation is triggered by some stimulus cue and the poor things find themselves trudging off on a foraging expedition despite a strong preference to languish in the hive. The point here is that, pace Dennett and Davidson, the intentional ascription can be made without commitment to any overall rationality of the bees. See Rey (, ) for further discussion.



The philosophy of animal minds 6. conclusion

Honeybees have abilities to navigate as a result of a remarkable integration of information about distance, time, direction, and the location of a range of items and landmarks, much of which they can convey to each other in their famous waggle dance. These abilities display a variety of systematicities regarding distance, solar bearing, and stimulus cues, which we specified in section . We submit that the best explanation of these abilities and systematicities is that the bees compute over causally efficacious, syntactically structured representations, i.e., representations in a “language of thought” of the sort proposed in classical theories of mental architecture. If this is so, then it is a reason to think that bees really do have the intentional states that researchers routinely ascribe to them.

chapter 5

Invertebrate concepts confront the generality constraint (and win) Peter Carruthers

This chapter defends the claim that invertebrates possess concepts against the so-called “generality constraint,” first proposed by Evans (). A number of different versions of that constraint are distinguished. Some are ill-motivated. Those that aren’t are not only consistent with but support the claim that invertebrates possess genuine concepts, as opposed to mere “proto-concepts,” and that invertebrates engage in genuine forms of thinking, as opposed to mere “proto-thinking.” Indeed, it turns out that distinctively human kinds of thinking contain mere faux-thoughts by comparison with those that we share with non-human animals. 1. introduction What does it take to possess a concept? Do any non-human animals have concepts? One crucial constraint on the concept concept is that concepts are the building blocks of thought. Hence no creature could count as a conceptuser that wasn’t capable of thinking. This mightn’t seem like a significant additional restriction, but actually it has some teeth, ruling out some otherwise concept-like phenomena. Consider the Australian digger wasp (Gould and Gould []). The female builds a tower-and-bell structure above the hole in which she lays her eggs, to protect them from another species of parasitic wasp. At various points during construction she uses her own body as a yardstick. For example, she stops building the tower and begins on the bell once the former is three of her own body lengths high. Does she, then, have the concept, three body lengths (or some sufficiently close analog)? She does at least possess a sort of recognitional capacity which she deploys to end one phase of her activity and initiate another. (And likewise she must be capable of recognizing the materials that she I am grateful to Elisabeth Camp, Bradley Rives, Michael Tetzlaff, and (especially) Robert Lurz for their comments on previous drafts of this chapter.





The philosophy of animal minds

collects for the construction of the tower, as well as recognizing that one side of the tower that she is constructing is higher than the other, and so forth.) But does the wasp believe that the tower is now three body lengths high, and is that why she moves on to the next activity (the construction of the bell)? There are multiple reasons for denying that she does. The most fundamental derives from the fact that the overall pattern of her behavior is rigid (albeit displaying flexibility of detail – collecting mud from here rather than there, placing the mud on this side rather than that side, and so on). For example, if an experimenter progressively buries the tower in sand while she is constructing it, she will just keep on building, indefinitely, because she never reaches the three-body-lengths trigger for initiating the next phase. But once she has completed the tower and begun on the bell, she takes no notice if an experimenter buries most of the tower in sand, even though the bell then ends up resting on the ground and will consequently be quite useless. Likewise if an experimenter makes a small hole in a completed portion of the tower before it is finished, she will build another tower-and-bell structure on top of it, rather than effecting a simple repair. Another consideration is that the state that ends the tower-construction phase never interacts with any other “goal” states. This gives us reason to think that the state isn’t genuinely a belief. For it is of the essence of beliefs that they should be apt to interact with desires in such a way as to issue in motor plans and behavior. Moreover, we have no reason to think that the state in question factors into two (or more) conceptual components, tower and three body lengths. But again, it is of the essence of beliefs that they should be structured out of distinct conceptual components, each of which can figure in other attitudes and be suitably combined with other concepts to formulate distinct thoughts. What the wasp actually has is an abstract, innately specified, but flexibly implementable, motor plan, which is guided in its detailed execution by perceptual information, and whose various stages are triggered and/or completed by the matching of concept-like recognitional templates against the perceptual data. From these considerations we can extract the following constraints. In order to count as having concepts, a creature needs to be capable of thinking. And that means, at least, that it must possess distinct belief states and desire states, which interact with one another (and with perception) in the selection and guidance of behavior. In addition, the belief states need to 

Editor’s note: See Saidel (chapter ) for a similar account of the distinctness condition for beliefs and desires, and Roberts (chapter ) for an account of concepts (employed in emotions) that are not detachable or recombinable in thought.

Invertebrate concepts and the generality constraint



be structured out of component parts (concepts) which can be recombined with others to figure in other such states with distinct contents. Moreover, belief and desire states need to play causal roles that are sensitive to their underlying structures, figuring in simple inferences that bring to bear belief states to select actions that will enable the realization of the creature’s goals. These constraints on concept possession are by no means trivial. Nevertheless, many invertebrates actually satisfy them (or so I shall argue in section ). This is especially clear in the case of honeybees, whose powers of thought have been intensively studied – notably their flexible use of spatial information in the service of a multitude of goals. But the constraints are probably satisfied by Australian digger wasps, too, in respect of the states that guide their navigational (but not their nest-construction) behavior. (And there is surely no requirement that all of an organism’s behavior should be guided by genuine concept-involving thoughts if any is to count as such. For much of our own routine, habitual, or “inconsequential” behavior wouldn’t pass muster, either.) I have argued for these claims in some detail elsewhere (Carruthers [, ]), and will only sketch those arguments here. (But see Tetzlaff and Rey [chapter ] for some closely related considerations.) My main focus will be on an argument purporting to establish yet further constraints on genuine concept possession (the so-called “generality constraint”), which invertebrates (together with most other animals) would turn out to fail. Let me say a word about terminology, however, before we proceed. The use of the term “concept” in philosophy is systematically ambiguous (Laurence and Margolis []). It is sometimes used to designate the content of a word or a component of thought. In this usage a concept is an abstract object, often identified with a “mode of presentation” of the things that the word picks out. But sometimes concepts are intended to be mental representations, concrete components of the physical tokenings of the thoughts of which they form part. In the present chapter I am concerned almost exclusively with concepts in the latter sense. Our question is whether invertebrates possess the sorts of mental representations that are the components of genuine thoughts. Whenever I want to refer to one of these mental representations I shall use italics (as well as using italics for 

There is also a third usage, which equates concepts with mental capacities. This is what Evans himself has in mind when formulating the generality constraint (, p. ). But I take this usage to be closely related to the idea of concepts as mental representations. For any capacity must have a categorical basis, and distinct capacities that bear systematic relationships to one another are likely to have distinct categorical bases (Fodor and McLaughlin []). The latter can then be equated with the mental representations postulated by language-of-thought theorists.



The philosophy of animal minds

emphasis – the difference should always be clear). And on those occasions when I do want to refer to the content of a mental representation I shall insert the relevant words or phrases within square brackets. 2. the case for invertebrate concepts Like many other insects, bees use a variety of navigation systems. One is dead reckoning (integrating a sequence of directions of motion with the distance traveled in each direction, to produce a representation of one’s current location in relation to the point of origin; see Gallistel []). This in turn requires that bees can learn the expected position of the sun in the sky at any given time of day, as measured by an internal clock of some sort. Another mechanism permits bees to recognize and navigate from landmarks, either distant or local (Collett and Collett []). And some researchers have shown that bees will, in addition, construct mental maps of their environment from which they can navigate. Gould () reports, for example, that when trained to a particular food source and then carried from the hive in a dark box to a new release point, the bees will fly directly to the food, but only if there is a significant landmark in their vicinity when they are released. (Otherwise they fly off on the compass bearing that would previously have led from the hive to the food.) Other scientists have found it difficult to replicate these experiments directly, perhaps because bees have such a strong disposition to fly out on compass bearings to which they have been trained. But in a related experiment, Menzel et al. () found that bees that had never foraged more than a few meters from the nest, but who were released at random points much further from it, were able to return home swiftly. They argue that this either indicates the existence of a map-like structure, built during the bees’ initial orientation flights before they had begun foraging, or else the learned association of vectors-to-home with local landmarks. But either way, they claim, the spatial representations in question are allocentric rather than egocentric in character. More recently, Menzel et al. () have provided strong evidence of the map-like organization of spatial memory in honey bees through the use of harmonic radar. The latter technology enabled the investigators to track the flight-paths of individual bees. Bees who were just about to set out for a feeder were captured and taken to random release points some distance from the hive. Initially, the bees then traveled along the vector that they were about to fly out on when captured. This was followed by a looping orientation phase, once the bees realized that they were lost, followed by

Invertebrate concepts and the generality constraint



a straight flight, either to the hive, or to the feeder and then to the hive. The latter sequence (a flight straight to the feeder), in particular, would only be possible if the bees could calculate a new vector to a target from any arbitrary landmark that they know, which requires both a map-like organization to their memory and the inferential resources to utilize it. Moreover, bees can make flexible use of the information encoded in their mental maps, in the service of multiple goals. Thus the very same information that there is nectar  meters north of the hive, for example, is utilized both when returning to the hive laden with nectar and when setting out to visit the nectar source from the hive once again. It is also used to guide the orientation of the bee’s dance to inform other bees of the location, and it can be acquired from observing the dance of another bee as well as from personal experience. In addition, bees learn the locations of many other substances that they take as goals when appropriate, including pollen, water, and tree sap. And the data obtained by Menzel et al. () suggests that all of these locations will be encoded on the bee’s mental map, in such a way that the bee could, if appropriately motivated, fly directly from a source of water to a source of tree sap, for example. There is much more that could be said about these and related data. To mention just one issue: does the fact that bees have memory systems with a map-like organization disqualify them from having genuine beliefs? This would be on the grounds that maps lack constituent structure, as Berm´udez (chapter ; a) claims. However, the latter claim might be true of some topographic maps, but certainly isn’t true of symbolic maps. These are composed of elements representing types of entity and substance (water, trees, grassland, buildings) as well as individuals (e.g., a particular river or town) which could be recombined with one another in indefinitely many distinct configurations to represent any number of different geographical layouts. The mental maps of bees are plainly of this latter sort. Taken all together, the data warrant the claim that bees possess both belief-like states and desire-like states that interact with one another in simple practical inferences to select and guide behavior; and that the belieflike states possess a component structure, containing symbols that refer to various landmarks and substances as well as encoding the distances and directions between them. Whether this is sufficient to qualify bees (and other invertebrates) as genuine thinkers, and genuine concept-users, is the topic of the remainder of our discussion. 

Editor’s note: See Rescorla (chapter ) for a similar account of cognitive maps as structured representations.



The philosophy of animal minds 3. the generality constraint

The generality constraint is introduced by G. Evans () as a constraint on genuine concept possession, and thus as a constraint on a creature’s capacity for authentic thought. (Others insist, in similar vein, on the “spontaneity” of thought; see McDowell [].) The constraint, as Evans formulates it, is this: genuine thinkers must be capable of entertaining all syntactically permissible combinations of any concepts that they possess (or almost all of them, at any rate – this qualification will be discussed in section ). So if thinkers possesses the concepts F, G, a, and b, then they must be capable of thinking each of the thoughts Fa, Fb, Ga, and Gb (but not the “thoughts” FG or ab, which are ill-formed and uninterpretable). A word about terminology before we proceed further. For the most part I shall discuss the generality constraint in terms of the thinking of thoughts. But Evans himself uses the language of entertaining a thought (see his [], p. ). Is this a significant difference? One possibility is that Evans uses “entertain” to mean something like “suppose,” in which case the generality constraint would be tantamount to the claim that any creature that has concepts must be capable of supposition. This idea will be discussed, and heavily criticized, in section . Alternatively, “entertain” (like “think”) can be used generically, to cover all forms of propositional attitude (believing, desiring, supposing, wondering whether, etc.). Thus understood, the difference between the two ways of formulating the generality constraint is merely verbal. And note that, so understood, the generality constraint permits “cross-overs” between the different attitude types. For what one might be incapable of believing one might nevertheless be capable of supposing or wishing. Thus someone who possesses the concepts a, identity, and negation might be capable of supposing that a isn’t a, even if she isn’t capable of believing it. And someone who is incapable of believing that he has never been born might nevertheless be capable of wishing it. Taken at its face, the generality constraint will very likely require us to deny thoughts and concepts to most if not all non-human animals. Hence, if it is endorsed, then animals will be capable, at best, of proto-thoughts composed of proto-concepts (Berm´udez [a]; Dummett [, ]). And the generality constraint will then mark a radical divide between the minds of human beings and the proto-mindedness of non-human animals. This is because, as a number of philosophers have pointed out, there are probably numerous restrictions on the ways in which the concepts (or rather, the proto-concepts) of animals can be combined and recombined. Carruthers () gives the following example. A bee that is capable of thinking that there is nectar  meters north of the hive (or something

Invertebrate concepts and the generality constraint



that approximates to this), and that is capable of thinking that the brood chamber is now above it, might nevertheless be incapable of thinking that there is nectar  meters north of the brood chamber. This is because the bee’s spatial navigation and mental map-building outside the hive are based on solar bearings, whereas bees navigate inside the hive in the dark, where they employ quite other (gravity-based and olfactory) ways of representing spatial relationships. And bees might very well lack any means of integrating the two sets of spatial representations into a single thought. Likewise, many who have written on this topic have used the example of a monkey who thinks that the lion is eating the antelope: it may nevertheless be incapable of thinking that the antelope is eating the lion. This example is probably not a good one, however. We might well be able to get the monkey to think that the antelope is eating the lion if we rigged things up right – for example, if we could arrange so that the antelope really was eating the lion, or at least appeared to be doing so. But there will be plenty of other examples that can serve to make the point. Thus a monkey that is familiar with an aged matriarch – call her “Elsa” – might be incapable of thinking that Elsa is an infant. For what could we possibly do that might induce the monkey to entertain such a thought (whether believing it, desiring it, or whatever)? The generality constraint is believed to be warranted by the demand that real thoughts must be compositionally structured. I endorse this demand. I agree that in order to count as a genuine thinker, a creature’s thoughts must be composed out of recombinable conceptual components. But there are a number of distinctions that have been overlooked in most treatments of the topic. One is between the different notions of possibility involved, which can be logical, causal, or metaphysical. And the other is between a strong requirement that genuine concepts must be recombinable with all (or almost all) syntactically permissible others, and the weaker requirement that genuine concepts must be recombinable with at least some others. Let me briefly consider the former set of distinctions first, although it is the difference between strong and weak versions of the generality constraint that is more important for my purposes. The requirement that it must be logically or conceptually possible for genuine concepts to recombine with others is too feeble to be of any interest. (Evans himself, however, sometimes seems to have had this reading in mind. For he writes that there should be no conceptual barrier in the way of thinkers entertaining the combinatorial variants of their thoughts: see his [], p. .) For even if a creature’s “thoughts” possess no component structure whatever – either realized in simple (componentless) sentence-like



The philosophy of animal minds

representations or in a distributed connectionist network with limited powers of learning – it will still be conceptually possible for that creature to entertain novel variants of the “thoughts” that it entertains. We just have to conceive that the creature should somehow acquire new powers of representation. In contrast, the requirement that it must be causally possible for genuine concepts to recombine with others is probably too strong for our purposes. For the generality constraint is meant to track the cognitive capacities that a thinking creature possesses, not its cognitive performance. For familiar reasons – having to do with contingent limitations of memory, attention, inferential skills, and so forth – it might be the case that a thinker is causally prevented from entertaining certain recombinations of its concepts, even though the creature possesses the underlying conceptual competence to do so. What we should claim, therefore, is that it must be metaphysically possible for genuine concepts to be recombined with others. This allows us to idealize beyond contingent limitations on a creature’s cognitive performance. But such idealization should be relative to the underlying cognitive architecture that the animal possesses. For what prevents a bee from combining solar-based spatial representations with gravity-based ones might be the modular organization of these two distinct kinds of spatial cognition, rather than mere limitations of memory or attention. In which case, to get those different forms of representation combined with one another would require a change to a novel (non-bee-like) cognitive architecture. And the resulting creature would, arguably, no longer be a bee. The distinction between causal and metaphysical varieties of the generality constraint may be by no means easy to negotiate in practice, involving, as it does, the question of what features of a creature’s cognition are essential to it, and what accidental. There are delicate issues here that may often be difficult to resolve. This won’t matter much for present purposes, however. Although I shall continue to formulate the generality constraint in terms of metaphysical possibility, I shall place little weight on its distinctness from a causal version of the same idea. Much more important is the question of the appropriate quantifier (all versus some) that should be employed. In fact we have the following two possibilities to consider. Strong generality constraint: If a creature possesses the concepts F and a (and is capable of thinking Fa), then for all (or almost all) other concepts G and b that the creature could possess, it is metaphysically possible for the creature to think Ga, and in the same sense possible for it to think Fb.

Invertebrate concepts and the generality constraint



Weak generality constraint: If a creature possesses the concepts F and a (and is capable of thinking Fa), then for some other concepts G and b that the creature could possess, it is metaphysically possible for the creature to think Ga, and in the same sense possible for it to think Fb. I maintain that the requirement that thoughts must be compositionally structured, built up out of distinct and recombinable concepts as parts, only warrants the weak generality constraint. But this raises no difficulty for the idea of invertebrate concepts. Crucially, compositionality does not warrant the strong generality constraint, which is the one that creates problems for the idea that any non-human animals are genuine concept users. Recall the claim that concepts are the building blocks of thought. Concepts are components of the complex representations that are thoughts. And if they really are the components of thought, then each such component must be capable of combining with at least some other concepts that the organism possesses in the context of a distinct thought. Conversely, if it were impossible for the concept F in Fa to combine with any other concept that the creature could possess, then that would suggest that either F or a (or both) aren’t really distinct isolable parts of the larger representational state. Genuine concepts should be detachable from the states of which they are parts. And if they are thus detachable, then there should be no principled obstacle to them figuring along with other such parts in at least some other complex states. The relationship between the weak generality constraint and compositionality is, arguably, an epistemic one. For consider what evidence could convince us that the concept F is a detachable component of a state with the content [Fa]. The best evidence would consist of cases in which that very concept figures in other thoughts, for example in a state with the content [Fb]. Hence if the weak generality constraint is satisfied, and we are satisfied that a creature’s behavior warrants ascribing an appropriate range of thoughts to it, then we have reason to think that it has thoughts that are built up out of component concepts. Otherwise we may lack any warrant for thinking that the creature’s state really does break up into the two separate components F and a. That is, for every concept F and every concept a that we want to attribute to the creature, we may need to find some other concepts G and b such that we have evidence that the creature is capable of thinking Fb and capable of thinking Ga. And the best such evidence is to actually find circumstances in which the creature thinks Fb and circumstances in which it thinks Ga.



The philosophy of animal minds

Hence it is, arguably, the weak generality constraint that warrants us in thinking that a creature’s thoughts are genuinely composed of concepts as parts. But this claim poses no threat to the conceptual capacities of invertebrates. Consider, for example, a honeybee’s thought with the content [nectar is  meters north of the hive] (or some near equivalent). Is this genuinely composed of the concepts nectar, 200 meters (or some roughly equivalent measure of distance), north (or some similar solar-based measure of direction), and hive? Well, yes, because we know that bees satisfy the weak generality constraint in respect of such concepts (Carruthers [, ]; Tetzlaff and Rey [chapter ]). We know that a bee can also think thoughts with the contents [the hive is  meters north of the nectar], [nectar is  meters west of the hive], [pollen is  meters north of the hive], and so on for all interpretable combinations of the four candidate concepts, both with each other and with other similar concepts. And we know that the inferences in which bees engage are sensitive to such component structures. What reason could there be for insisting that genuine concept-users must also satisfy the strong generality constraint, and be capable of combining any concept that they possess with any other concept that they possess? For this certainly isn’t required by the core idea that concepts are the building blocks of thought. Of course, from our human perspective, our thought processes are the very paradigm of thinking. And the strong generality constraint, or something close to it, really is true of us. But we need to pay attention to the reason why it is true of us. I shall argue that this is best explained by our capacity for creative supposition, combined with our abilities to draw inferences from the things that we suppose. But thinking creatively is one thing, thinking simpliciter is another. The fact that most animals can’t do the former provides no reason for denying that they can do the latter. There is therefore no good reason, I shall argue, to believe that a creature must be capable of entertaining all permissible combinations of its concepts in order to count as a genuine concept-user, or a genuine thinker. In which case non-human animals can count as full-fledged thinkers, after all (invertebrates included). 4. strong generality as an ideal As many have pointed out, not even humans really satisfy the strong generality constraint, if the latter is taken with full generality. For although they are syntactically well-formed, we can’t actually interpret or do anything with such thoughts as Julius Caesar is a prime number or Green ideas sleep furiously. One response to this point has been to claim that the generality constraint shouldn’t require all syntactically possible combinations of

Invertebrate concepts and the generality constraint



concepts, but only those that conform to the right categories. Thus for these purposes the concept prime number, to be real, only needs to be combinable with other number concepts, not with any singular terms whatever. This is the line that is generally taken in the philosophical literature, following Evans (). Camp () argues that placing categorical restrictions of this sort on the generality constraint is a mistake, however. For combinations of concepts that seem like nonsense in one era, and which would therefore have motivated a categorical restriction, can not only be interpreted in another, but can even be recognized to be true. The thought Matter is energy would provide one clear example, and metaphors like Juliette is the sun would provide another. Rather, Camp thinks, the strong generality constraint should be thought of as an ideal to which actual organisms approximate (and to which humans come pretty close). On this account, then, the question whether non-human animals have concepts might not always admit of a yes-or-no answer. Rather, most animals might meet the generality constraint to some degree, and can therefore be described as approximating to genuine thought and concept deployment to a greater or lesser extent. Although concept possession is here regarded as a matter of degree, there are surely positions at either end of the spectrum where stronger, yes-or-no language would be warranted. Thus humans approximate so closely to the ideal set out in the strong generality constraint that it would be misleading to say anything other than that we are genuine thinkers and genuine concept-users (just as it would be misleading to describe someone with a slightly receding hairline as anything other than hirsute, or not bald). And conversely, many animals fall so far short of meeting the strong generality constraint that it would be quite inappropriate to describe them as having concepts at all (just as it would be inappropriate to describe someone with only a few tufts of hair over his ears as hirsute, and equally misleading to deny that he is bald). Rather, the language of “proto-concepts” and “protothoughts” is better warranted when describing most species of non-human animal (Camp []). Camp thinks, then, that the strong generality constraint is an ideal to which organisms can approximate. The use of evaluative language, here, prompts one to ask: Ideal with respect to what? In the service of what value? Camp replies: with respect to the purposes for which thought is required. Her idea is that a creature whose capacities for representation fall a long way short of what the generality constraint would require isn’t getting the full benefit from those capacities. But of course, whether a capacity to generate novel combinations of concepts counts as a benefit must be relative to the



The philosophy of animal minds

cognitive powers (and also the ecological niche) of the creature in question. There is no benefit in being able to entertain a new range of thoughts if you can’t do anything useful with those thoughts. But this is, I claim, a function of the range of attitudes available to the creature, as well as its inferential abilities, not its capacity to think per se. I shall elaborate these points in turn. 4.1. Creative supposition What is it that enables humans to approximate to the strong generality constraint? There is no single answer: it is actually a cluster of abilities. One is our capacity to suppose – to entertain a thought without commitment to its truth, evaluating it or drawing inferences from it. This capacity is first manifested in infancy in the form of childhood pretend play, as when the child (perhaps struck by the similarity in shape between a banana and a telephone handset) supposes that the banana is a telephone, and pretends accordingly (Nichols and Stich []). No other species of animal on earth engages in pretence in normal circumstances. (Some hand-reared chimpanzees have, as adults, been observed to engage in behavior that at least looks very much like pretence, however. See Jolly [].) And it is extremely doubtful whether any animals outside of the great ape clade are capable of supposing. (There is some reason to think that chimpanzees might occasionally entertain states that are the functional equivalent of supposition, at least, mentally rehearsing potential actions in advance of decision-making. See Carruthers [].) Moreover, the capacity to generate suppositions creatively is a crucial ingredient in human problem-solving abilities. Consider, for example, its role in science, specifically in our practices of inference to the best explanation. When confronted by puzzling data, scientists often need to generate a range of potential explanations before devising experiments to test between the resulting candidate theories. This will often require considerable creativity, since explanatory hypotheses can’t in any sense be derived from, or “read off from,” the data. Indeed, there is no way to routinize hypothesis generation. For example, consider what took place when scientists first hypothesized that light is a wave, or that light is a stream of particles. Such ideas were, at the time, genuinely novel. It isn’t just in science that creative supposition is important to us. The same is true in much of ordinary life. And the same is equally true of huntergatherers. For as Liebenberg () demonstrates, hunters when tracking prey will often need to develop speculative hypotheses concerning the likely causes of the few signs available to them. (And these can be extremely subtle,

Invertebrate concepts and the generality constraint



such as the precise manner in which a pebble has been disturbed, say, or the way in which a blade of grass has been bent or broken.) These hypotheses are then subjected to extensive debate and further empirical testing by the hunters concerned. Constructing these hypotheses will often require creative uses of imagination, since they concern the unobserved (and now unobservable) causes of the observed signs, and the circumstances in which they may have been made. It should be plain, in fact, that a capacity for creative supposition forms an essential component of human life-history, entering into almost everything that is distinctive of us and our unique forms of flexibility and adaptability. It is also plain that it provides a significant part of the explanation for the fact that human thought approximates to the strong generality constraint, since it is what enables us to put together old ideas in novel ways. (And it may be, in turn, our unique capacity for recursively structured language that underlies this capacity, to a significant degree. For we can, at will, select novel combinations of lexical items to be formulated into a sentence, which we can then rehearse and consider. See Carruthers [, a] for extensive discussion.) In light of this account of why human forms of thinking approximate to the strong generality constraint, it should be plain that the latter is simply irrelevant to the question whether a creature is capable of genuine thought (i.e., possesses beliefs and desires), and likewise irrelevant to the question whether a creature is a genuine concept-user. What it is relevant to is the question whether a creature is capable of certain kinds of thought, specifically suppositional and creative thought. But supposition is a distinctive type of attitude. The question whether a creature is capable of supposing that P has no bearing on the question whether it is capable of belief, and capable of desire. And likewise the question whether a creature is capable of freely and creatively generating novel thoughts/suppositions is irrelevant to the question whether it is capable of thought per se, and to the question whether it possesses concepts. To put the same point somewhat differently: the reason why humans approximate to the strong generality constraint isn’t because they have concepts, and because there is something about the nature of concepts, or the nature of propositional attitudes, that requires it. It is rather because we are – perhaps uniquely in the animal kingdom – capable of supposition, and of creatively generating thoughts to be taken as objects of supposition. (And even if our unique capacity for language isn’t itself responsible for the latter, it certainly greatly enhances it.) Someone might respond to these points by proposing a doctrine of the unity of the attitudes (modeled after Aristotle’s doctrine of the unity of the



The philosophy of animal minds

virtues). The claim would be that a creature can’t possess any attitude type unless it possesses all. Hence a creature doesn’t really count as having beliefs or desires unless it is also capable of supposing. But what could possibly motivate such a claim? One suggestion would be that we take humans as our paradigm cognizers, and then subject the different types of human attitude to functional definition. In which case, since beliefs can interact with creative suppositions in humans, it will be of the essence of beliefs that they should be capable of doing so. I have two things to say by way of reply. One is that it is very doubtful whether we should select the human mind as our paradigm of what a mind is like. (I shall return to develop this point in section .) The other is that, even if we do do this, we plainly shouldn’t define the different attitude types holistically, in terms of their interactions with all others. For consider the consequences: if it is true that psychopaths are incapable of guilt (as it seems to be; see Blair []), then it would turn out that they are incapable of believing anything or desiring anything, either (and nor would they count as having concepts). And even if it were felt that one could reply to this objection by noting that psychopaths are defective members of the species (which is actually far from clear: they might rather be in balanced dimorphism with normal folk; see Murphy and Stich []), then consider Mr. Spock from the television series Star Trek. He is said to be incapable of emotion. Is he thereby incapable of thinking at all? Does he have no beliefs, and no concepts? This would plainly be an absurd thing to say. These points leave an opening, of course, for someone to claim that possession of beliefs and desires doesn’t require a creature to be capable of all other attitude types, but just creative supposition. That would still leave most non-human animals beyond the pale of genuine thought. But this claim looks equally ill-motivated (and for the same reason). And it is likewise subject to counterexample. We just have to imagine a human who is especially “literal minded,” being capable of all other attitude types except creative supposition. (It may be that some autistic people are actually like this. Certainly one of the diagnostic features of autism is that autistic children fail to engage in pretend play.) Yet it would surely be absurd to deny that such a person was capable of thinking at all. 4.2. Two systems of reasoning I have argued that one reason why human thinking approximates to the strong generality constraint doesn’t have anything much to do with our

Invertebrate concepts and the generality constraint



possession of concepts, as such, but rather with our capacity for creative supposing. Yet this is by no means the whole story. For there would be little point in entertaining creative suppositions if we couldn’t also develop new and flexible reasoning strategies for drawing inferences from those suppositions. If we were limited to the same set of fixed inferential capacities employed by non-human animals, then arguably creative supposing would bring us little advantage. (What would a chimpanzee be able to do with the hypothesis that light consists of particles, for example, even if it could be induced, somehow, to entertain such a thought?) But we aren’t so limited. Indeed, there is a growing consensus in cognitive science that humans possess a unique system for reasoning and drawing inferences. Let me elaborate. It is now widely accepted by those who work on the psychology of reasoning that humans possess two different types of cognitive system for thinking and reasoning (Evans and Over []; Kahneman []; S. Sloman [, ]; Stanovich []). Most believe that what is now generally called “system ” is really a collection of different systems that are fast and unconscious, operating in parallel with one another. The principles according to which these systems function are, to a significant extent, universal to humans, and they aren’t easily altered (e.g., by verbal instruction). Moreover, the principles via which system- systems operate are, for the most part, heuristic in nature (“quick and dirty”), rather than deductively or inductively valid. It is also generally thought that most, if not all, of the mechanisms constituting system  are evolutionarily ancient and shared with other species of animal. System , on the other hand, is generally believed to be a single system that is slow, serial, and conscious. The principles according to which it operates are variable (both across cultures and between individuals within a culture), and can involve the application of valid norms of reasoning. These system- principles are malleable and can be influenced by verbal instruction, and they often involve normative beliefs (that is, beliefs about how one should reason). Moreover, system  is generally thought to be uniquely human. There is an important sense, then, in which distinctively human reasoning abilities (realized in system ) are socially constructed. This is, no doubt, a large part of the explanation of the fact noted earlier, that thoughts that seem “nonsensical” in one era can be made sense of, and found to be true, in another. But again, this has nothing to do with the capacities to entertain thoughts, or to possess concepts, per se. Whether a creature is capable of having beliefs built up out of component concepts is one



The philosophy of animal minds

thing, and whether it possesses an indefinitely flexible socially constructed reasoning capacity is surely quite another. 5. proto-thought versus faux-thought Someone might seize upon the distinction drawn above between system and system- thinking to propose that genuine thinking and genuine concepts should be reserved to system , with the sorts of system- thoughts and concepts that we share with the rest of the animal kingdom being described as mere proto-thoughts and proto-concepts. For after all, doesn’t our own human case constitute the very paradigm for both the concept thought and the concept concept? One thing wrong with this proposal is that there is actually nothing special about most of the concepts that get deployed in creative supposing, or in system- thinking more generally. On the contrary, most system- thoughts are built up out of some of the same system- concepts that might be available to a non-human animal. Thus many animals might possess the concepts light and particle, for example, which figure in the thought Light consists of particles. And while it is true that there are also many distinctively human concepts, perhaps arrived at – either directly or indirectly – via the operations of system , the generality constraint was never supposed to be about the number of concepts that a creature possesses. Rather, the question at issue in the strong generality constraint is whether a creature’s concepts can be combined with all other concepts that it can have. So the fact that humans possess many more concepts than do invertebrates is, in itself, no objection to the claim that the latter possess genuine (as opposed to proto-) concepts. (Indeed, in light of this point, it seems likely that invertebrates might approximate to the strong generality constraint much more closely than do other non-human animals, who possess a wider range of concepts. If invertebrate concepts are largely drawn from the domain of navigation, it may well be that most such concepts are capable of being combined in thought with most others. If the minds of monkeys, in contrast, are highly modular [Carruthers ()], containing multiple specialist systems for forming beliefs of particular types, then there might actually be many more restrictions on their capacities to combine together concepts drawn from these different domains. Hence, although monkeys possess many more concepts than do honeybees, it may be that the latter come closer to complying with the strong generality constraint.)

Invertebrate concepts and the generality constraint



Another thing wrong with the proposal that system- thinking should be taken as the paradigm of thought is that system  doesn’t really constitute a natural kind (nor a set of natural kinds). Rather it is, as we pointed out above, to a significant extent socially constructed. Its operations are highly variable between cultures, and between individuals within a culture. (In some people, for example, system  is largely verbal, running on rehearsed natural-language sentences in the form of “inner speech,” while in others it is largely visual, consisting of sequences and manipulations of visual images.) And the operations of system  depend, to a significant extent, upon the individual subject’s normative beliefs about how she should reason. The system- processes that we share with non-human animals, in contrast, do constitute a cluster of natural kinds. There are systems for doing dead reckoning, for extracting geometrical information about the environment, for judging the approximate numerosity of a set, for calculating rates of resource-availability, and so on and so forth (Gallistel []); as well as systems for generating novel desires, of various types, and for selecting appropriate actions in the light of one’s desires (Carruthers []). These systems, when present, will be universal to all members of the species, and many will be highly conserved across species. Each represents a legitimate object of scientific study: our goal should be to figure out how each such system works, how it is structured, and how it interacts with other systems in the mind–brain of the organism in question. Moreover, the most plausible accounts of system  on the market maintain that its processes are actually realized in the operations of system , to a significant degree, rather than existing alongside of the latter (Frankish []; Carruthers [, a]). For example, it is by interpreting a given utterance in inner speech as a commitment, or a “making up of mind,” that the functional equivalent of a new belief, desire, or intention results (depending on the sort of commitment that gets self-attributed) – provided, that is, that one has a standing system- desire to execute one’s commitments. Thus, suppose that an utterance is interpreted as a commitment to the truth of the proposition that it expresses, and the subject therefore forms a system- belief that a commitment of that kind has been made. This will then interact in future with the system- desire to honor commitments, issuing in further overt or covert verbalizations. If asked whether he believes the proposition in question, for example, the subject will reply that he does. For one of the things that one ought to be prepared to do, if one has committed oneself to the truth of a proposition, is assert it. And likewise, during one’s system- practical or theoretical reasoning,

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The philosophy of animal minds

one will be prepared to rely upon the sentence in question as a premise. For if one has committed oneself to the truth of a proposition, then one ought also to commit oneself to any other proposition that one believes follows from it. And so on. Thus although system- thinking is the variety that is most familiar to us (since its operations are to a significant degree conscious), it is system  that is the more basic, both ontologically and for explanatory purposes. For system- processes are largely realized in those of system . (One difference, in my view, is that system  always involves the activity of the motor system, whereas system  needn’t do so. See Carruthers [a].) In which case, although ordinary folk might be inclined to take system- thinking as their paradigm of thought, it is plain that they shouldn’t do so – or not for explanatory purposes or in the context of cognitive science, at any rate. Even more importantly, it turns out that there aren’t really any attitudes at the system- level (Carruthers [b, b]). For the events that occur within system  don’t occupy the right sorts of causal roles to be a judgment, for example, or to be a decision. Thus it is surely a requirement on any event that is to qualify as a judgment that it should be immediately available to inform the agent’s practical reasoning, without the mediation of any further cognitive process. For a judgment is supposed to be the event that brings a new belief into existence. For example, judging P ⊃ Q issues in a belief with that content, and should be available to interact with a desire for Q immediately, issuing in a desire to bring about P, without the intervention of any other belief or desire. But a system- event like the inner verbalization of the sentence “If P then Q” plays no such role. On the contrary, it first has to issue in a belief that one has committed oneself to the truth of the proposition if P then Q, and then this together with one’s desire to execute one’s commitments leads one to feel committed to wanting to bring about P in the presence of a desire for Q. And since what happens in system  doesn’t really contain or involve any propositional attitudes, as such, there is good reason for us to insist that system- thinking consists of mere faux-thoughts, rather than in the real thing. Instead of it being the case that animals possess mere proto-thoughts in comparison to human forms of thinking, then, distinctively human thoughts are mere faux-thoughts compared to those that we share with nonhuman animals. Each individual token of system- thinking is a real enough event, of course, involving the activation of real (system-) concepts. And such events – individually and collectively – can have immensely important

Invertebrate concepts and the generality constraint

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consequences. (Witness the march of scientific discovery and technological invention.) But the processes that issue in such events aren’t natural kinds, and nor are there any beliefs and desires at the system- level. From the standpoint of cognitive science, then, distinctively human thinking consists of mere faux-thoughts. The real thing is done by animals (and by humans insofar as they share the same cognitive systems as other animals).

chapter 6

A language of baboon thought? Elisabeth Camp

1. introduction Does thought precede language, or the other way around? How does having a language affect our thoughts? Who has a language, and who can think? These questions have traditionally been addressed by philosophers, especially by rationalists concerned to identify the essential difference between humans and other animals. More recently, theorists in cognitive science, evolutionary biology, and developmental psychology have been asking these questions in more empirically grounded ways. At its best, this confluence of philosophy and science promises to blend the respective strengths of each discipline, bringing abstract theory to bear on reality in a principled and focused way. At its worst, it risks degenerating into a war of words, with each side employing key expressions in its own idiosyncratic way – or worse, contaminating empirical research with a priori dogmas inherited from outmoded philosophical worldviews. In Baboon Metaphysics (), Dorothy Cheney and Robert Seyfarth offer an analysis of baboon cognition that promises to exemplify the very best interaction of philosophical theory and empirical research. They argue that baboons have a language of thought: a language-like representational medium, which supports the sophisticated cognitive abilities required to negotiate their complex social environment. This claim is intended to be surprising in its own right, and also to shed light on the evolution of spoken language. Because our own ancestors likely lived in a similarly complex social environment, Cheney and Seyfarth propose that the earliest humans also developed language first as a cognitive medium, and that spoken language evolved as a means to express those thoughts. There are two potential difficulties here. First, “Language of Thought” (LOT) is a term of art, with much associated theoretical baggage and often comparatively little careful exposition. Thus, evaluating the claim requires getting clearer about just what LOT implies in this context. Second, if 

A language of baboon thought?

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Cheney and Seyfarth are right, then we seem to be left with a rather surprising model, on which baboons think in a way very like we do but fail to talk. According to Cheney and Seyfarth, this